Colorectal cancer consensus molecular subtype classifier codesets and methods of use thereof

ABSTRACT

Provided herein is a consensus molecular subtype (CMS) classifier for colorectal cancer patients. Also provided are methods of using the classifier to identify a clinically beneficial therapeutic regime for each patient as well as methods of treating a patient accordingly Custom Nanostring code sets, which work on formalin-fixed, paraffin-embedded samples, are provide for use in determining the CMS for a colorectal cancer patient.

REFERENCE TO RELATED APPLICATIONS

The present application claims the priority benefit of U.S. provisional application No. 62/828,098, filed Apr. 2, 2019, the entire contents of which is incorporated herein by reference.

REFERENCE TO A SEQUENCE LISTING

The instant application contains a Sequence Listing, which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Mar. 27, 2020, is named UTFCP1374WO_ST25.txt and is 46.2 kilobytes in size.

BACKGROUND 1. Field

The present invention relates generally to the fields of medicine and oncology. More particularly, it concerns compositions and methods for classifying colorectal cancer as well as using such classification in treating patients having colorectal cancer.

2. Description of Related Art

Colorectal cancer is the third most common cancer and a leading cause of cancer death worldwide. Patients with stage III colon cancer have a 60 to 70% chance of remaining disease-free, with slightly over 50% relative risk reduction with the use of adjuvant 5-FU and oxaliplatin-based regimens. As a result, approximately 1 in 5 patients derive benefit with adjuvant treatment, and for the remaining four patients, the use of adjuvant therapy results in added toxicity with no benefit. Many low-risk stage III patients may opt for single agent 5-FU therapy given the modest absolute risk reduction and added toxicity from oxaliplatin. Conversely, the early identification of high-risk patients allows a more informed discussion about the risks and benefits of adjuvant therapy and a more risk-adapted patient management. Many efforts have been made to identify reliable risk factors to assess individual risk of these patients, including the use of gene expression signatures.

Current efforts in the NCI Colon Cancer Task Force of the GI Steering Committee are focused on identification of high-risk stage III colon cancer and efforts to escalate intensity of adjuvant chemotherapy. This was identified as one of the top three priorities for CRC research in 2015-2016 by the Colon Cancer Task Force. This current push recognizes that there are many high-risk stage III patients that are not being adequately treated with the current regimens given their high recurrence rate. Conversely, international efforts are ongoing to identify the proper intensity of adjuvant therapy including discussions about over-utilization of oxaliplatin for low-risk stage III patients (as exemplified by the CALGB 80702 study of 3 months of FOLFOX chemotherapy instead of 6 months) and treatment intensification (in the ECOG EA2153 study to escalate to FOLFOXIRI in high-risk patients) (Kurniali et al., 2014; Yaffee et al., 2015). Several reviews have reiterated the importance of prognostication in stage III colon cancer, given the increased recognition of long-term toxicities and heterogeneity of outcomes in the population (Goel et al., 2014; Kelley et al., 2011).

The two most commonly utilized popular and commercially available assays are Oncotype DX (Genomic Health Inc.) and Coloprint (Agendia Inc.). These assays were developed as prognostic biomarkers in stage II and III colon cancers. The Coloprint assay in validation studies demonstrated a five-year relapse-free survival rate for low risk patients of 87.6% for high-risk patients of 67.2% (Kennedy et al., 2011). The hazard ratio in the multivariate model was 2.69 (P=0.003). While this assay provides strong prognostic information, the assay requires fresh frozen tissue for analysis. Although a prospective study is ongoing, the fresh tissue requirement precludes practical application in the community. Efforts to transfer this signature from an Agilent array to formalin fixed paraffin embedded samples using the same platform have not been successful and are no longer being pursued. The Oncotype DX assay in contrast has been designed to utilize FFPE samples and utilizes a 13-gene RT-PCR technique that is more robust to sample degradation. In the validation cohorts for this assay the interquartile range was fairly narrow in the continuous recurrence score and resulted in a hazard ratio of 1.38 (P=0.004) (Goel et al., 2014). A second validation looking at the NSABP C07 study utilized tertiles of risk and identified only as an 8%-9% difference in absolute risk between the groups in stage III colon cancer. Other tools have not been as well studied and have similarly poor performance in multivariate overall survival models in mixed cohorts of stage II and III colon cancer (Mittempergher et al., 2011; Kelley et al., 2011). As such, there is a clear need for better tools to prognosticate stage III patients to help with implementation of value-based patient management decisions.

SUMMARY

Provided herein is a gene set and customized list of gene probes for use in classifying patients with colorectal cancer into one of four consensus molecular subtypes (CMS) (as defined by Guinney et al., (2015)) based on either fresh frozen (FF) or formalin-fixed paraffin embedded (FFPE) samples taken from the patient's primary tumor, applicable in the single sample clinical setting. This can be used to identify patients with poor prognosis to target for more aggressive therapy as well as identify subsets of patients for administration of novel targeted therapy based on CMS-specific biology.

In one embodiment, provided herein are methods of classifying a cancer status of a subject having colorectal cancer, comprising: (a) obtaining a tumor sample from the subject; (b) measuring an expression level of a plurality of genes in the tumor sample, wherein each gene in the plurality of genes is selected from Table 1; (c) generating an expression profile based on a comparison between the expression level of the plurality of genes in the sample from the subject and a corresponding expression level obtained from a reference sample derived from a different subject having a known cancer status; and (d) categorizing the cancer status of the subject based on the expression profile. In some aspects, step (c) comprises applying a weighted support vector machine to the expression level of the plurality of genes.

In some aspects, the plurality of genes comprises 75 genes selected from Table 1. In some aspects, the plurality of genes comprises 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, or 195 genes selected from Table 1. In some aspects, the plurality of genes comprises all 200 genes selected from Table 1. In some aspects, the plurality of genes comprises the 75-gene classifier from Table 1. In some aspects, wherein the plurality of genes comprises the 100-gene classifier from Table 1. In some aspects, the cancer status is categorized as CMS1, CMS2, CMS3, CMS4. The CMS classification may be determined based on a weighted support vector machine applied to these gene sets, which gives a probability of each CMS type for the sample, and classification occurring if the probability is greater than, for example, 0.50 for any CMS type.

In some aspects, if the cancer is a CMS1 cancer, then the following genes tend to be downregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: ATP9A, WFDC2, VAV3, CEBPA, LEFTY1, DIDO1, CHN2, PRAP1, FITM2, NOL4L, SHLD1, FAM84A, DACH1, ABAT, DPEP1, ARID3A, PRR15, ATP10B, PLCB1, QPRT, AMACR, DAPK2, FRZB, PRDXS, MYRIP, GNG4, ILDR1, GRM8, GUCY2C, HUNK, GPR153, ASCL2, GPR143, PBX1, PCP4, ACSL5, OTULINL, PPP1R14D, RNF43, PRLR, GPCPD1, SHROOM4, PTPRO, ACE2, TSPAN6, TNNC2, CACNA1D, SLC30A2, CAB39L, PPP1R14C, AXIN2, IMMP2L, CTTNBP2, SPIRE2, RETNLB, GGH, NR1I2, GYG2, SEMASA, CDHR1, MAGED1, and HEPH. In some aspects, if the cancer is a CMS1 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: ENO2, KCTD1, TRNP1, NUCB2, SLC25A37, ERRFIl, MCUB, RARRES3, TFAP2A, SLC4A11, APOL1, IFIT3, ZWINT, DEPDC1, PAK6, TNS4, PRC1, CDCA2, TMEM64, HSPA4L, GTF2A2, KCNK1, ASPH, MT2A, PNP, ADGRG6, RAB27B, BST2, DOCK5, TRIM7, USP14, and SOCS6.

In some aspects, if the cancer is a CMS2 cancer, then the following genes tend to be downregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: RAMP1, CDC42EP2, CTSE, ENO2, PALLD, KCTD1, HLA-E, TRNP1, NEDD9, NUCB2, SLC25A37, ERRFIL MCUB, PPP3CA, RARRES3, TCN1, TFAP2A, MLPH, SLC4A11, REG4, APOL1, LMO4, and CREB3L1. In some aspects, if the cancer is a CMS2 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: VAV3, CEBPA, CHN2, PRAP1, FITM2, NOL4L, SHLD1, DACH1, ABAT, DPEP1, ARID3A, PRR15, QPRT, AMACR, DAPK2, PRDXS, MYRIP, GNG4, GRM8, HUNK, ASCL2, GPR143, ACSL5, OTULINL, PPP1R14D, RNF43, PRLR, GPCPD1, SHROOM4, PTPRO, ACE2, TSPAN6, TNNC2, CACNA1D, SLC30A2, CAB39L, PPP1R14C, AXIN2, IMMP2L, CTTNBP2, SPIRE2, GGH, NR1I2, GYG2, SEMASA, CDHR1, FARP1, CEL, TOMM34, PIGU, FAM122B, EREG, MAPRE1, ZDHHC23, CKAP2, SESN1, EIF6, MLLT3, PITX2, EPB41L4B, NDFIP2, PDP1, PPP1R3D, PCMTD2, UPF3A, SRPK1, CDCA7, ANKRD27, DPM1, CPNE1, and JADE3.

In some aspects, if the cancer is a CMS3 cancer, then the following genes tend to be downregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: SLCO2B1, AHNAK2, LDLRAD3, ZCCHC24, OSTM1, IFIT3, MPP1, CSGALNACT2, RBMS1, and CYTH3. In some aspects, if the cancer is a CMS3 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: WFDC2, RETNLB, NEDD9, TCN1, MLPH, REG4, CREB3L1, MRAP2, ZG16B, SAMD5, BCL2L15, PIGR, ST6GALNAC1, SLC9A2, FAM3D, RNF183, CRYM, CDC42EP5, SERPINB1, TMEM61, GALNT8, FOXA2, KLK1, HES2, FBXO34, MAP2K6, RAP1GAP, XBP1, CA8, and ASRGL1.

In some aspects, if the cancer is a CMS4 cancer, then the following genes tend to be downregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: PDZK1IP1, CCNO, ANP32B, AGR2, CENPE, POLD3, POP1, KIF2C, ZWINT, MRAP2, TC2N, ZG16B, FABP5 PLCH1, NCAPH, GFPT1, SAMD5, BCL2L15, PIGR, CARMIL1, DEPDC1, ASF1B, ST6GALNAC1, PAK6, RAN, SLC9A2, UGT8, PALB2, UTP15, TNS4, TNFRSF11A, PRC1, and ARHGAP32. In some aspects, if the cancer is a CMS4 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or another CMS subtype: PBX1, PCP4, PALLD, AHNAK2, ZCCHC24, CSGALNACT2, RBMS1, CYTH3, PDZD2, HLX, DACT1, ZNF415, C3orf14, and ZSCAN18.

In some aspects, if the cancer is a CMS1 or CMS3 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or a CMS2 or CMS4 cancer: GALNT5, FUT8, SLCO1B3, and CEP192. In some aspects, if the cancer is a CMS1 or CMS4 cancer, then the following genes tend to be upregulated compared to a reference sample derived from a healthy subject and/or a CMS2 or CMS3 cancer: ACSL5, OTULINL, PPP1R14D, and RNF43. In some aspects, if the cancer is a CMS2 or CMS3 cancer, then the expression of ESRP1 is upregulated compared to a reference sample derived from a healthy subject and/or a CMS1 or CMS4 cancer. In some aspects, if the cancer is a CMS2 or CMS4 cancer, then the expression of HOXD11 is upregulated compared to a reference sample derived from a healthy subject and/or a CMS1 or CMS3 cancer.

In some aspects, the expression level of the plurality of genes is measured by detecting a level of mRNA transcribed from the plurality of genes. In some aspects, the expression level is measured using nanostring probes. In certain aspects, the nanostring probes hybridize to the target sequence listed in Table 1 for each of the plurality of genes. In some aspects, the expression level of the plurality of genes is measured by detecting a level of cDNA produced from reverse transcription of mRNA transcribed from the plurality of genes. In some aspects, the expression level of the plurality of genes is measured by detecting a level of polypeptide encoded by the plurality of genes. In some aspects, the sample is a formalin-fixed, paraffin-embedded sample. In some aspects, the sample is a fresh frozen sample.

In some aspects, the methods further comprise reporting the cancer status of the subject. In some aspects, the reporting comprises preparing a written or electronic report. In some aspects, the methods further comprise providing the report to the subject, a doctor, a hospital, or an insurance company.

In one embodiment, provided herein are methods of assessing a likelihood of a subject having colorectal cancer exhibiting a clinically beneficial response to treatment, the method comprising: (a) obtaining a cancer status determined according to the method of any one of the present embodiments; and (b) assessing a likelihood of the cancer exhibiting a clinically beneficial response to treatment based on the cancer status.

In some aspects, the methods further comprise reporting whether the subject is likely to exhibit a clinically beneficial response to treatment. In some aspects, reporting comprises preparing a written or electronic report. In some aspects, the methods further comprise providing the report to the subject, a doctor, a hospital or an insurance company.

In some aspects, if the subject is determined to have a CMS1 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram. In some aspects, if the subject is determined to have a CMS2 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with cetuximab, EGFR inhibitors, or HER2 inhibitors. In some aspects, if the subject is determined to have a CMS3 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with cetuximab, EGFR inhibitors, or HER2 inhibitors. In some aspects, if the subject is determined to have a CMS4 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.

In one embodiment, provided herein are methods of treating a patient having colorectal cancer, the method comprising obtaining a cancer status determined according to the method of any one of the present embodiments and administering an anti-cancer therapy to the subject. In some aspects, the anti-cancer therapy is a chemotherapy, a radiation therapy, a hormonal therapy, a targeted therapy, an immunotherapy or a surgical therapy.

In some aspects, if the subject is determined to have a CMS1 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram. In some aspects, if the subject is determined to have a CMS2 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor. In some aspects, if the subject is determined to have a CMS3 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor. In some aspects, if the subject is determined to have a CMS4 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.

In one embodiment, provided herein are compositions comprising a set of nanostring probes that hybridize to the target sequence for at least 75 of the genes listed in Table 1. In some aspects, the compositions comprise nanostring probes that hybridize to the target sequence for at least 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, or 190 of the genes listed in Table 1. In some aspects, the compositions comprise nanostring probes that hybridize to the target sequence for all 100 of the genes in the 100 gene set listed in Table 1. In some aspects, the composition comprises nanostring probes that hybridize to the target sequences for all 100 genes in the 100 gene set listed in Table 1. In some aspects, the composition comprises nanostring probes that hybridize to the target sequences for at least 100, 110, 120, 130, 140, 150, 160, 170, 180, 185, 190, or 195 of the genes in the 200 gene set listed in Table 1. In some aspects, the composition comprises nanostring probes that hybridize to the target sequences for all 200 genes in the 200 gene set listed in Table 1.

As used herein, “essentially free,” in terms of a specified component, is used herein to mean that none of the specified component has been purposefully formulated into a composition and/or is present only as a contaminant or in trace amounts. The total amount of the specified component resulting from any unintended contamination of a composition is therefore well below 0.05%, preferably below 0.01%. Most preferred is a composition in which no amount of the specified component can be detected with standard analytical methods.

As used herein the specification, “a” or “an” may mean one or more. As used herein in the claim(s), when used in conjunction with the word “comprising,” the words “a” or “an” may mean one or more than one.

The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” As used herein “another” may mean at least a second or more.

Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, the variation that exists among the study subjects, or a value that is within 10% of a stated value.

Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

FIG. 1. Persistence of Structure in CMS groups. Heatmap of top 472 genes (rows) for discriminating CMS (columns) for independent training and validation cohorts, with columns sorted within CMS and rows sorted by clustering genes in the training data.

FIG. 2. Classification accuracy for training data V1 (based on 4-fold cross validation; left panel) and the full validation data V2 (right panel) for various methods as function of number of genes.

FIG. 3. Classification Confidence of Affymetrix Validation Samples vs. Classification Accuracy of 472-gene wSVM classifier: Distribution of classification confidence (a) across 713 validation samples (top), and classification accuracy (1=correct, 0=incorrect) as a function of classification confidence (a), with fitted loess curve and 95% confidence interval.

FIGS. 4A&B. Sample-wise and Gene-wise correlation of FF/FFPE paired Samples. FIG. 4A—Histogram of sample-wise Spearman correlation of paired FF/FFPE values across all 472 CMS genes on Nanostring assay, with threshold of 0.75 marked with red vertical line. FIG. 4B—Histogram of gene-wise Spearman correlation of paired FF/FFPE values based on samples with sample-wise correlation>0.75, with thresholds to determine the top 100 and top 200 genes indicated by red and blue vertical lines, respectively.

FIGS. 5A&B. Association of Sample-wise FF/FFPE with RNA Quality: Scatterplot of gene-specific Spearman correlation of FF/FFPE vs. RNA quality of FF samples (FIG. 5A, based on RIN) and FFPE samples (FIG. 5B, based on % with 200 nt).

FIG. 6. Sample-wise and Gene-wise correlation of FF/FFPE paired Samples: Scatterplot of gene-specific Spearman correlation of FF/FFPE computed separately for first batch (CRCSC samples N=85) and second batch (non-CRCSC samples N=73), in terms of FF/FFPE correlation computed across all samples. Note the high level of agreement across batches, that genes with high FF/FFPE correlations for one batch tended to also have high FF/FFPE correlation in the other batch, suggesting high FF/FFPE correlation is a characteristic of the gene/probe set.

FIGS. 7A-D. Nanostring CMS Classifier Performance. Performance of Nanostring 100 gene CMS classifier applied to FFPE samples (FIGS. 7A&C) and FF samples (FIGS. 7B&D). FIGS. A&B plot 4-class accuracy vs. classification confidence, with the dots marking individual samples either correctly (1.0) or incorrectly (0.0), with color indicating correct CMS. The line contains a generalized additive model (GAM) fit to these data with 95% pointwise confidence bands and demonstrates that samples classified with greater confidence were more likely to be correctly classified. FIGS. C&D plot 4-class accuracy vs. RNA quality, defined as %200 nt (FFPE) or RIN (FF). Note that there is little if any association of CMS accuracy with RNA quality, suggesting that the performance of classifier is robust to RNA quality in this study.

FIG. 8. Performance of CRC CMS-200 in CLIA-certified Molecular Diagnostic Laboratory. The reproducibility of the assay was determined by review of CMS calls for 24 samples that were run across 11 runs generating 120 separate reactions. One hundred and thirteen of 120 (94%) had expected results and correct CMS call. The average standard deviation of the gene expression score across all CMS was ±0.012. The average standard deviation across CMS1 was ±0.004, CMS 2 was ±0.022, CMS 3 was ±0.001 and CMS 4 was ±0.023.

FIG. 9. Inter run reproducibility and inter tech reproducibility.

FIG. 10. CMS subtype and overall survival in stage IV colorectal cancer.

DETAILED DESCRIPTION

Using a network analysis combining information across six previously published subtyping systems and 4,151 samples from 18 studies, Guinney et al. (2015) found that colorectal cancer patients tend to cluster within four consensus molecular subtypes (CMS) that appear to have different molecular characteristics based on RNA expression data from fresh frozen colorectal cancer patient samples. These CMS provide prognostic information, provide predictive information for current treatments, and allow for the partitioning of the disease into biologically distinct subgroups that can be characterized and investigated to discover new subtype-specific precision therapy strategies. In order to realize this potential, reliable methods to classify patients into CMS based on formalin-fixed paraffin-embedded (FFPE) samples from primary colorectal cancers are essential. As such, provided herein are novel custom-designed Nanostring code sets that yield accurate CMS classifications for FFPE samples based on mRNA and/or non-coding RNA (e.g., miRNA) expression.

First, a CMS classifier for fresh frozen (FF) samples was constructed and validated using data from the colorectal cancer subtyping consortium (CRCSC; Guinney et al., 2015). A rigorous, detailed investigation was performed to get an optimized predictor in which numerous classification approaches were compared. The best predictor was chosen based on cross-validation of a training data set, and then its classification accuracy was assessed in a validation data set as a function of the number of genes in the model, with genes ranked according to a multi-class boosting algorithm. Custom Nanostring code sets were designed based on the best probes for the genes in this classifier, and then these code sets were run on 152 paired FF/FFPE samples, with 78 from patients among the consensus cohort in Guinney et al. (2015) and 74 others for which an Affymetrix classifier was also run on FF samples to obtain pseudo-gold standard CMS. The Nanostring FFPE-based classifier was developed by first identifying a subset of genes with high correlation between FF and FFPE samples, using a subset of the CRCSC data set to retrain the FF-based classifier based on this reduced set of genes, and then transforming the measurements of the Nanostring FFPE assay to the scale of the Affymetrix FF data from the CRCSC samples so that classifier could be directly applied. This strategy allowed for efficient use of available information, taking advantage of the large CRCSC data set to train a stable model and using the smaller paired FF/FFPE data to select the genes with high correlation across the two sample types and assess classification accuracy on FFPE samples by comparing with the gold standard CMS. The performance of the classifier was validated on multiple platforms, including Affymetrix, RNAseq, and Nanostring FF or FFPE.

Based on cross-validation, the best classifier was found to be a weighted support vector machine, which had outstanding CMS predictive accuracy in the validation data set (>0.93) for models with at least 75 genes. To move forward, a 472-gene model was chosen that had 4-group CMS classification accuracy of 95.9%, an improvement over the classifiers presented in Guinney et al. (2015) (94.2% and 93.2% for random forest and single sample classifier, respectively), and custom Nanostring codesets were designed based on the optimal Affymetrix probe for each gene along with 28 housekeeping genes with high expression, low variance, and non-differential expression across CMS. The gene expression values from the Nanostring codesets were found to be reproducible (median CV=0.11), and 100/472 codesets had a FF/FFPE correlation of at least 0.80, reproducibly across cohorts. The resulting Nanostring FFPE-based classifier based on this 100-gene signature had four-group classification accuracy of 80.4%, with samples classified with higher confidence having greater accuracy, but with performance stable across mRNA quality/quantity. The Nanostring classifier also performed well on FF samples, with four-group classification accuracy of 81.0%.

This Nanostring FFPE-based CMS can be used to stratify patient samples by CMS to administer CMS-based precision therapy strategies.

I. Consensus Molecular Subtypes for Colon Cancer

The inventors have participated in an international consortium (CRCSC) whose primary goal was to identify consensus molecular subtypes of colorectal cancer, based on an aggregative network analysis of RNA expression and independent of patient treatments or outcomes. Through an unprecedented level of data sharing across eighteen CRC gene expression data sets (n=4,151 patients) and analytical collaboration, this group identified four consensus molecular subtypes (CMS), each with its own distinguishing characteristics (Guinney et al., 2015; Dienstmann et al., 2017). CMS1 (Immune, 17.5%) is characterized by enrichment in hypermutation, hypermethylation, microsatellite instability, and immune activation. CMS2 (Canonical, 42%) demonstrates canonical CRC characteristics, including epithelial differentiation, MYC and WNT activation, and high levels of chromosomal instability. CMS3 (Metabolic, 13%) is epithelial and showed high levels of metabolic dysregulation, with higher rates of KRAS mutation. CMS4 (Mesenchymal, 27.5%) shows characteristics of epithelial mesenchymal transition (EMT), activation of TGFβ, angiogenesis, and prominent reactive stroma. These subtypes were persistently present in all data sets, were reproducible, and robustly defined independent of the assay used to measure RNA expression. While certain molecular patterns are enriched within specific subgroups (BRAF, MSI, KRAS), these mutations do not fully recapitulate the subtypes and substantial heterogeneity remains within many molecular subtypes, as epitomized by KRAS mutation. The consensus subgroups do substantially incorporate subgroups previously defined by closely related tumors that do not carry the alteration in question (so-called MSI-like, and BRAF-like gene expression signatures) (Missiaglia et al., 2014; Tian et al., 2012).

Prognostic significance of CMS4: Despite the fact that these subgroups were discovered based on global biology, and not any prognostic or predictive considerations, these CMS have been found to have potential prognostic and predictive value in precision therapy strategies. First, CMS4 has potential as a prognostic biomarker for stage II-III that can be useful to stratify patients for clinical trials or select patients for more aggressive treatment. Guinney et al. (2015) showed that CMS4 (mesenchymal) demonstrates clearly worse progression free and overall survival in stage II-III patients relative to other CMS and has the potential to identify a subset of poor prognosis patients for more aggressive or alternative therapy. These results were affirmed in the PETACCIII study (Marisa et al., 2017). Patients with Stage III have a 60%-70% chance of remaining disease free, with a >50% relative risk reduction with use of adjuvant 5-FU and oxaliplatin-based regimens. Clinical information on 475 stage III patients from seven clinical cohorts was used to define the CMS groups, with an individual median follow-up time of 4.9y (range 0.5y-16.7y). Based on Cox regression, CMS4 patients had higher risk of death than non-CMS4 patients (HR=2.26, p<0.001, 95% CI 1.41-3.61) after adjusting for known prognostic factors: age, pT, pN, gender, and MSI status. Stage III CMS4 patients had a 5-year survival rate of 0.53 (95% CI 0.44-0.64), while non-CMS4 patients' 5-year survival rate was 0.74 (95% CI 0.68-0.79). For stage II-III patients, CMS4 also had higher recurrence risk than non-CMS4 (H=1.73, p=0.002, 95% CI 1.22-2.46). This is consistent with three studies that have shown similar poor prognosis in stage II and III colon cancer with mesenchymal signatures (Marisa et al., 2013; Roepman et al., 2014; Budinska et al., 2013).

Better performance than OncotypeDX Colon: As the Oncotype DX is the most commonly studied gene expression assay, a prognostic score was constructed based on the published genes and algorithm (Kennedy et al., 2011) using expression data from these samples. Dichotomizing these scores (High=top 25%, Low=bottom 75%), only marginal significance (HR=1.69, p=0.02, 95% CI 1.09-2.62) was found after multivariate analysis, similar to prior independent validation in clinical trials (HR=1.34). CMS4 remained an independent prognostic factor even after adjusting for OncotypeDX score in addition to prognostic factors: age, pT, pN, gender, and MSI status (HR=2.27). From these results, the CMS4 subgroup was found to be associated with worse overall survival for stage III colon cancer, and represents a robust, clinically meaningful assay. CMS1 also is a potentially poor prognostic group for metastatic or post-relapse CRC patients. Guinney et al. (2015) showed that CMS1 (Immune) patients had worse overall survival than CMS2-4 after relapse, and this agreed with results from two large clinical trials showing that for metastatic patients, CMS1 patients had significantly worse survival than patients in the other CMS groups (Stintzing et al., 2017; Lenz et al., 2017). Given these results, CMS1 may be a subgroup to target for more aggressive treatment following relapse.

Potential CMS-based targeted therapy strategies: In addition, there have been extensive efforts in the past several years in the CRC research and pharmaceutical communities to understand the specific molecular biological characteristics of each CMS and put this information to work to find whether CMS represent subgroups differentially responding to existing therapies, or to develop CMS-specific therapy strategies designed to target the CMS-specific biological characteristics. There is also evidence of the potential of CMS being predictive of response to existing therapies. Mesenchymal features have been associated with resistance to adjuvant chemotherapy (Roepman et al., 2014). For example, CMS4 may be predictive in determining oxaliplatin benefit in C-07, where patients are randomized to 5-FU+/−oxaliplatin. Preclinical experiments demonstrate that mesenchymal transition is associated with reduced dependence of EGFR signaling (Barr et al., 2008). In certain high-risk subgroups, cetuximab may improve outcomes in the adjuvant setting (Taieb et al., 2014). In the metastatic setting, preliminary data demonstrate a statistically significant impact of CMS4 on cetuximab sensitivity in a single agent study (HR 2.0, P=0.03, in KRAS wild type). Results from the randomized CALGB/SWOG 80405 study (Lenz et al., 2017) found that CMS1 KRASwt patients had longer OS and PFS on FOLFOX/FOLFIRI+bevacizumab than FOLFOX/FOLFIRI+cetuximab, and that CMS2 KRASwt patients had longer OS on FOLFOX/FOLFIRI+cetuximab than FOLFOX/FOLFIRI+bevacizumab, while results from the randomized FIRE3 study (Stinzing et al., 2017) found that CMS4 KRASwt patients with FOLFIRl+cetuximab had significantly improved OS and PFS than patients given FOLFIRI+bevacizumab. Together, these suggest that CMS may be useful for selecting patients for cetuximab therapy. Similarly, the CMS impact is being explored in randomized studies of regorafenib, where increased benefit in CMS3 was seen in the CORRECT study (HR 0.29, P=0.01) with validation planned in the CONCUR study. Similarly, irinotecan (PETACC-3) and ziv-aflibercept (VELOUR) differential efficacy is being explored, and positive results would prompt increased need for a CMS assay to validate and apply to the clinic. Collectively, these demonstrate the substantial predictive benefit that CMS can provide for CRC therapy.

Another exciting application of CMS is the potential of identifying new CMS-specific targeted therapies that may be discovered after deeply characterizing the distinct molecular biology underlying the various CMS. As CMS-specific targets and preclinical models are developed, there is potential for CMS to lead to the discovery of new precision therapy strategies that would have been missed without consideration of CMS. For example, suppose there is a treatment targeting the biology of one of the less prevalent CMS (e.g., CMS3 at <15%) that has a dramatic response rate (e.g., 33%) among CMS3, but no response in other CMS. Absent CMS, this treatment would appear to have little to no activity in the overall CRC cohort (e.g., <5% in our example) and may not be further studied, thus missing an extremely effective targeted therapy for a sizable subset of CRC patients. Incorporation of CMS in studies will allow the identification any such potential targeted therapies and offers hope of CMS-driven precision therapy strategies in the future.

Need for clinical CMS classifier device: In order to realize these potential benefits from CMS, it is necessary to have a robust, reliable classifier to discern a CRC patient's CMS from their primary tumor tissue. Guinney et al. (2015) presented a Random Forest (RF) classifier that involved 5,973 genes and was built primarily using microarrays designed for use with fresh frozen (FF) samples. No efforts were made in that paper to build or optimize a more parsimonious classifier using fewer genes. A 5,973-gene classifier is not practical for clinical use, and more importantly the widespread clinical adoption and use of tissue-based markers is practical only through the use of FFPE tissue, as FF samples are generally not available in a community practice setting. The poor reproducibility of microarray-based genetic signatures between FF and FFPE has been a major challenge in moving gene expression signatures into a standard of care setting in colon cancer (Al-Mulla et al., 2011) and other solid tumors (Al-Mulla et al., 2011; Maes et al., 2013; Wood et al., 2010; Wright et al., 2003; Xue et al., 2015). Formalin fixation induces protein-nucleic acid crosslinks modifying the RNA and high temperature during the tissue embedding accelerates and strengthens these crosslinks (Dietel et al., 2013; Jacobs et al., 2012a; Jacobs et al., 2012b; Potluri et al., 2015). Long-term storage in paraffin blocks leads to fragmentation of RNA and DNA (Dietel et al., 2013). These factors and others lead to discordant measurements between FF and FFPE samples, and this effect can vary by gene or by probe location. Nanostring nCounter platform for gene expression analysis has shown flexibility and reproducibility with low quality RNA from FFPE tissue samples (Veldman-Jones, et al., 2015). Guinney et al. (2015) presented another classifier intended for use with single samples (single sample predictor, SSP), but this classifier is also primarily trained and validated on fresh frozen samples, and involves 693 genes, more than one would ideally want to use in a clinical assay.

II. Summary of FFPE-Based Classifier Development

Summary of CMS Classifier: A CMS classifier has been developed that can be applied to FF or FFPE samples and obtains accurate CMS classifications for individual patient samples. This assay utilizes custom Nanostring code sets that have a strong FF/FFPE correlation such that they are appropriate for use with each sample type. This algorithm can also successfully classify FF samples using other gene expression platforms, including Affymetrix arrays and RNAseq, and the ideas can be applied to other platforms including HTG and any others that emerge. As detailed below, extensive studies have been performed to validate this classifier, first in the FF setting for Affymetrix or RNAseq data, and then in the FFPE and FF setting using Nanostring code sets.

Development and validation of 472-gene FF-based classifier: A rigorous study was performed to identify a parsimonious, robust, and reliable CMS classifier primarily for fresh frozen (FF) samples using training and validation data from the colorectal subtyping consortium (CRCSC) used in Guinney et al. (2015). The model was trained using a data set consisting of 1,329 samples from 12 studies, and validated in a cohort of 1,329 samples from 14 studies that included subsets to test out-of-sample predictive accuracy (383 samples from two studies), RNAseq accuracy (189 samples from one data set), and Affymetrix accuracy (713 samples from 11 studies). The CMS signal was remarkably consistent across these two data sets (FIG. 1). In these validation data, a 472-gene classifier was found to have 96.3% 4-class accuracy in the validation data set, with similar levels of accuracy in the Affymetrix, RNAseq, and out-of-sample subsets. This classifier gives a probability for each CMS, with the CMS with the highest probability chosen, and the probability for the chosen CMS being called the classification confidence. A vast majority (75%-80%) of samples were classified with high confidence (>90%), and these were nearly always classified correctly, with 1,076/1,078 (99.8%) of the samples in the validation data correctly classified, and many of the “incorrectly” classified samples with lower degrees of confidence in fact appeared to have evidence of being a mixture of CMS (which is expected in 15%-20% of samples according to Guinney et al. (2015)), and in those cases, the gold standard CMS was nearly always among those with substantial probabilities.

Development and validation of 100-gene FFPE-based classifier: Next, a custom Nanostring code sets was designed based on the list of 472 genes from this classifier. These code sets were run on 85 paired FF/FFPE samples that were part of the CRCSC data set and so had gold standard CMS calls, and 73 others for which Affymetrix FF samples were also run to get pseudo-gold standard CMS calls. Rather than building a de novo classifier from this relatively small study with 158 samples, a strategy was used to select a subset of 100 genes having concordant signals in paired FF and FFPE measurements, and then the CMS classifier was retrained using the 1,329 samples in the CRCSC training data set and these 100 genes, using sample-specific quantile normalization to deal with the single-sample setting, and then applying this classifier to the Nanostring data. A subset of 100 genes/probe sets (see Table 1) with high Spearman correlation across paired FF/FFPE values (median=0.730) demonstrated that this correlation is consistently high across batches and thus a characteristic of the gene and probe set, and this set of genes was used to define the classifier. This classifier demonstrated 80% 4-class accuracy for FFPE samples, with this accuracy increasing to 86% and 89% for samples classified with high levels of confidence (80% and 90% confidence respectively). This performance was robust to measures of RNA quality.

TABLE 1 200-gene FFPE-based classifier. One asterisk next to the Gene Name indicates inclusion in the 100-gene FFPE-based classifier and two asterisks indicates inclusion in the 75-gene FFPE-based classifier. Gene Name Accession Position Target Sequence ABAT** NM_000663.4 5004-5103 TTAGCTAGAAGAATTTCAAGGAAAAGAAT TCTCAGCAGAGCTCAAGATTGTAGAAACT CAGCAGAAGCTGGTAAAAACATGGGGAGC CCGGAGGACAGGC (SEQ ID NO: 1) ACE2** NM_021804.2 2958-3057 AGTTGAAAACAAGGATATATCATTGGAGC AAGTGTTGGATCTTGTATGGAATATGGAT GGATCACTTGTAAGGACAGTGCCTGGGAA CTGGTGTAGCTGC (SEQ ID NO: 2) ACSL5 NM_203379.1 2110-2209 CCAACATTGAAAGCAAAGCGAGGAGAGCT TTCCAAATACTTTCGGACCCAAATTGACA GCCTGTATGAGCACATCCAGGATTAGGAT AAGGTACTTAAGT (SEQ ID NO: 3) ADGRG6** NM_001032394.2 6230-6329 AACCAGATGAGAAAAAAATTAAGAAATTG CTCAAGGGAAACATTTGTAAATGGATTTG AAAGATTGAGCCAAATTCTGTTGTCAGTT CTAAGCATGCAGT (SEQ ID NO: 4) AGR2** XM_005249581.3 1213-1312 TAAATTTTAGTCAGATTTTGCCCAACCTA ATGCTCTCAGGGAAAGCCTCTGGCAAGTA GCTTTCTCCTTCAGAGGTCTAATTTAGTA GAAAGGTCATCCA (SEQ ID NO: 5) AHNAK2 NM_138420.2 17559-17658 TAAAGGCTACACACACATATGGAGCACCC CATCCCACAGCACATTACATCCACCTCAC TTCACAGAACGGAGAACAGAGCAGAAATG ACCAGAACACCTT (SEQ ID NO: 6) AMACR** 236365_at.1  68-167 ATACTTGCTGGGGATACCATAATGAACAA AACAGACCTGTTCTCCGCTCTTGAGGAAA TCAAAGACAAACACAGGATATGGAATAAA CCCAGAATTATCT (SEQ ID NO: 7) ANKRD27 NM_032139.2 4001-4100 GAAACATCCCTTTTTGTTGAGAACCTCCC TTGAATGTCTGTCACACTCACACCTGACG GGATGGTTACTGGATTAGAGAGTAGATTT GGCACATCTTTTC (SEQ ID NO: 8) ANP32B* NM_006401.2 1297-1396 ACCAAGCTTGTGGACTTCACCCCAACAAA ATTGTAAGCGTTGTTAGGTTTTTGTGTAA GATTCTTGCTGTAGCGTGGATAGCTGTGA TTGGTGAGTCAAC (SEQ ID NO: 9) APOL1 NM_001136540.1 2465-2564 TGAAGTCTTTCCCTGGTGATGGTCCCCTG CCCTGTCTTTCCAGCATCCACTCTCCCTT GTCCTCCTGGGGGCATATCTCAGTCAGGC AGCGGCTTCCTGA (SEQ ID NO: 10) ARHGAP10 NM_024605.3 2733-2832 AGCACCTGCTGCTGCGATTTTAAAGGGAA CTGTACTACTCGCAGTGATAGGTTTGCAG AGTGTGTGCTTGGCTGTGGCAGCCTAGCT TGGAGAAGCTGCT (SEQ ID NO: 11) ARHGAP32 XM_011543075.1 6479-6578 GTTCCTAAACCAGAGTACGAGGTCCCTGG GAATTTAAGTAGCTACGCATTATCTATTA TTAGACTGCAAGTTCCTGCAATAACTGCT TAGTTCACAGCCC (SEQ ID NO: 12) ARID3A* NM_005224.2 2401-2500 GGCGTTCAGGCAGCCCTGATGGACCGAAG GCTCTGGTGTCTGGTTTGGCCCCACAGCA GTGTGGGCCGATCCTGTTTACCTCATACA TCCCTGCACTGTG (SEQ ID NO: 13) ASCL2** NM_005170.2 1471-1570 CGGGGGGCACCAACACTTGGAGATTTTTC CGGAGGGGAGAGGATTTTCTAAGGGCACA GAGAATCCATTTTCTACACATTAACTTGA GCTGCTGGAGGGA (SEQ ID NO: 14) ASF1B** NM_018154.2 1134-1233 GTCAGTGCCTGTCAAGGCTCCAGTCCTGC TGAGCCAAAGGCTTTGTCATTCCTTTCTC TTCCTGTACATCTGAGCAGACCCACTCCA GCTTTCTGGTGTC (SEQ ID NO: 15) ASPH** NM_001164751.1 1918-2017 GCCTTTGGCTAATTGAGTAATTCCCCTCC AGCACTAGAGACCGCTCAGTGCTCTTACT AGATGAACTCAGTAACGCCTTGAGCTGGG TTGATTGAGGATG (SEQ ID NO: 16) ASRGL1* NM_025080.3 1141-1240 CAGCCGCCAAGGACGGCAAGCTGCACTTC GGAATTGATCCTGACGATACTACTATCAC CGACCTTCCCTAAGCCGCTGGAAGATTGT ATTCCAGATGCTA (SEQ ID NO: 17) ATP1OB NM_025153.2 7038-7137 ACCTGCAAGTTGATTAGAACTGCCTTTCT TCCCAGGCTTGACATAGGTATTAAGTCAA AATTACATGAAACCCAGTGGTAAAAAAGC CTCTGAAAGCTGT (SEQ ID NO: 18) ATP9A** NM_006045.2 7093-7192 AAATTTGGTTTTGAATGAACCTGCAAAGC ATCCTGCAGCGTGAGCAGCTCCTCCACCT GGAGCTCCGAAGCATCTTCTCAGGCCAAA GCGGCATTACCCG (SEQ ID NO: 19) AXIN2** NM_004655.3 3902-4001 ATATAGTGTACGGCAAAAGAGTATTAATC CACTATCTCTAGTGCTTGACTTTAAATCA GTACAGTACCTGTACCTGCACGGTCACCC GCTCCGTGTGTCG (SEQ ID NO: 20) BCL2L15* NM_001010922.2 3364-3463 TATTTTAAGAGACTCTATCTTAGGAGAGC TTAAGTGATTGGGCTGCAGGAAGAAGACA TTGTAACCCAGGAATTAAAAATGGATTCA GATTGCCTGATTT (SEQ ID NO: 21) BCL6 NM_001130845.1 3109-3208 ATTAAAAATATAAAACTGCGTTAAAGGCT CGATTTTGTATCTGCAGGCAGACACGGAT CTGAGAATCTTTATTGAGAAAGAGCACTT AAGAGAATATTTT (SEQ ID NO: 22) BST2** NM_004335.2 561-660 GAAGCTGGCACATCTTGGAAGGTCCGTCC TGCTCGGCTTTTCGCTTGAACATTCCCTT GATCTCATCAGTTCTGAGCGGGTCATGGG GCAACACGGTTAG (SEQ ID NO: 23) C20orf196 NM_152504.2  987-1086 TCTCTGAGGAGTAATTTATGCTCTAGCAC TCCCTTTCCTCTAGATCGGCCTGAGGCTG GGACATTACATGAAATCACACCCTTGCTG GGCTTAATCCCTT (SEQ ID NO: 24) C3orf14** NM_001291942.1 271-370 AGGATATAGAAGCAGCAGAAAAGTCACTA CAGACCAGGATTCACCCACTTCCACGGCC TGAGGTGGTTTCTCTTGAGACTCGTTACT GGGCATCAGTAGA (SEQ ID NO: 25) CA8 NM_004056.4 662-761 GGAGCTCCATCTGATCCACTGGAACTCCA CTCTGTTTGGCAGCATTGATGAGGCTGTG GGGAAGCCGCACGGAATCGCCATCATTGC TCTGTTTGTTCAG (SEQ ID NO: 26) CAB39L** NM_001287339.1 1087-1186 AACTCATTGAGTTTCTGAGCAGCTTCCAA AAAGAAAGGACGGATGATGAGCAGTTCGC TGACGAGAAGAACTACTTGATTAAACAGA TCCGAGACTTGAA (SEQ ID NO: 27) CACNA1D NM_000720.3 7282-7381 AGTTTACATAAGAGAATATCACTCCGATG GTCGGTTTCTGACTGTCACGCTAAGGGCA ACTGTAAACTGGAATAATAATGCACTCGC AACCAGGTAAACT (SEQ ID NO: 28) CCDC109B* NM_017918.4 741-840 CCCTTGAACAGGTGAAAGCTGGAATAGAA GCTCATTCGGAAGCCAAAACCAGTGGACT CCTGTGGGCTGGATTGGCACTGCTGTCCA TTCAGGGTGGGGC (SEQ ID NO: 29) CCNO NM_021147.4 1122-1221 GAGGCGGCGCTGGAGGACTGTATGGGCAA GTTGCAGCTGCTGGTGGCCATAAACAGTA CTTCCTTGACTCACATGCTGCCCGTTCAG ATCTGCGAGAAGT (SEQ ID NO: 30) CDC42EP2 NM_006779.3 1779-1878 AGGGCTTTGTGGAGGACAGGCCTTGCCCT CAAGAACGTCGTACCTGACGCTGAGCCTG TCATGAGAATGCAACAGGAGCAAACCAAG TGTTGCTGTGACA (SEQ ID NO: 31) CDC42EP5 NM_145057.2 775-874 AGCTGAACGACGTCATCGGCCTCTAGGTT CCCTCATTCCCCGCGCCCTTCCCGCCCGG CACCCCACTTCTGTATACATAAACGGCCA AGGTGTGTGCCCG (SEQ ID NO: 32) CDCA2** XM_011544419.1 2904-3003 TAAAGATTTGTCTGATGCCATTGAGCAAA CCTTTCAGAGGAGAAATAGTGAAACCAAA GTGCGACGTAGCACGAGGCTACAGAAGGA TTTAGAAAACGAA (SEQ ID NO: 33) CDCA7* NM_031942.4 2093-2192 TTCTTCTGCCCGAAGGGTAAGTGGTGCGT CCAGCTTACACAATCATAATTCAAAGGTT GGTGGGCAATGTAATACTTAATTAAAATA ATGATGGAAGAGC (SEQ ID NO: 34) CDHR1** NM_033100.3 4941-5040 CAGGAATTGGGAGGCCTAGGGTGGGCATG AAAGCTTGGGAAGCACTGTCGTCTCTCAG ACAGGCGTCCTAAAGACCTCTAGGCTGGA AGCTTGGGCTTGC (SEQ ID NO: 35) CEBPA NM_004364.4 2055-2154 CTCAGCCTTGTTTGTACTGTATGCCTTCA GCATTGCCTAGGAACACGAAGCACGATCA GTCCATCCCAGAGGGACCGGAGTTATGAC AAGCTTTCCAAAT (SEQ ID NO: 36) CEL** NM_001807.4 2236-2335 CCCCACAGATGACTCCAAGGAAGCTCAGA TGCCTGCAGTCATTAGGTTTTAGCGTCCC ATGAGCCTTGGTATCAAGAGGCCACAAGA GTGGGACCCCAGG (SEQ ID NO: 37) CENPE* NM_001813.2 8080-8179 TCAAGTTTAGGCCTTTGTCCAGAGGTGCA AAATGCAGGAGCAGAGAGTGTGGATTCTC AGCCAGGTCCTTGGCACGCCTCCTCAGGC AAGGATGTGCCTG (SEQ ID NO: 38) CEP192 NM_032142.3 7347-7446 GAGGCTTCTGCTCGCATACCTGAGCAGCT TGATGTGACTGCTCGTGGAGTTTATGCCC CAGAGGATGTGTACAGGTTCCGGCCGACT AGTGTGGGGGAAT (SEQ ID NO: 39) CHN2 NM_001293081.1 2191-2290 ACTCACCAGTCTTGCTTTGGAGTGAGCAG AAGAGAATGACTATTTTACGTGGAGCATC ATTGTGTGACTGTTGACCTGGACAGTCCC AAGGGCTATGCAG (SEQ ID NO: 40) CKAP2 NM_001098525.2 2785-2884 CCCAGACTTGTGTTCTCTTGCGTCCCTTG GACTGCCTGTTGATTGATGGAAAGTGTCT GCACTGACACTTTTCGTCAGTAGTCTGTA GTTTCGTGGCCTC (SEQ ID NO: 41) CPNE1** NM_001198863.1 1426-1525 TGACGGATGTGGAAGCCACACGTGAGGCT GTGGTGCGTGCCTCGAACCTGCCCATGTC AGTGATCATTGTGGGTGTGGGTGGTGCTG ACTTTGAGGCCAT (SEQ ID NO: 42) CREB3L1* NM_052854.2 2554-2653 CCCCTGCTGCTGCCCAAGCCGCTGGGCCT TTTTAATTGCCAAACTGCTCTCTTCATCA GCTCAGCACATGCTTTAAGAAAGCAAAAC CAAAAAAAAAAAA (SEQ ID NO: 43) CRYM NM_001014444.2 833-932 CTGAGCTGGGAGAAGTGATTAAGGGAGTG AAACCAGCCCACTGTGAGAAGACCACCGT GTTCAAGTCTTTGGGAATGGCAGTGGAAG ACACAGTTGCAGC (SEQ ID NO: 44) CSGALNACT2 NM_018590.4 3013-3112 GGAGAGAAAAAGCAAATGGTATGCCACAA GATCACTCTGATTTGAGAAAAGGGAGGAG GGGAAGATAGTCTGAATGGAAATCTGAAA TACGGAATGTTTT (SEQ ID NO: 45) CTSE NM_001910.2 2071-2170 TTTGTGGCAAAAATACTTCCTAGGTGGTG CTGGGTACTTCTTGTTGCATCCTGTCAGG AGGCAGATAATGCTGGTGCCTCTCTATTG GTAATGTTAAGAC (SEQ ID NO: 46) CTTNBP2** NM_033427.2 5010-5109 GAAATCAACAACAACTCAAAAGAAGTGAA TTGGAACTTACACAAAAATGAACACCTAG AAAAACCTAACAAATAGGCCTGCCTACAA TATTCTCATTATT (SEQ ID NO: 47) CYTH3 NM_004227.3 3967-4066 TGCTGGCCGCCCTGGTTCATATTTGAGTT TAATTGTACTGACCCTGGACCCAGATAAG CAGCAACTTTGTGTCTTTGGGGTCACAGA ACATTTTGGGGCA (SEQ ID NO: 48) DACH1** NM_080759.4 4909-5008 AATGTCTAGTTGTTCTATATTATAACCAC ATTTGCGCTCTATGCAAGCCCTTGGAACA GAACATACTCATCTTCATGTAGGACCTAT GAAAATTGTCTAT (SEQ ID NO: 49) DACT1 NM_001079520.1 3351-3450 AGAGACATTCACTATTAATGAAGTAACCC TTGGGCATGACTCCAATCCCAGAATTGCT CACTGAGCGCTATGCCACCGAAGCGTTGA CCTGAACATATTA (SEQ ID NO: 50) DAPK2 NM_014326.3 1364-1463 AATTCTCCATAAAATGGGCTTTCCTCTGT CTGCCATCCTCAGAGTCTGGGGTGGGAGT GTGGACTTAGGAAAACAATATAAAGGACA TCCTCATCATCAC (SEQ ID NO: 51) DEPDC1** NM_001114120.2 3455-3554 TTAATCAAGGGAGATACACCTATCAGATG TTTAAAATAACAACACTACCCACTGAAAT CAGGGCATATAGAATCATTCAGCTAAAGA GTGACTTCTATGA (SEQ ID NO: 52) DIDO1** NM_022105.4 2238-2337 AAGTATTCGTATTCTCTTCATCCCAGTCT GATTGCATAGCCACACTGCCCGGCACGCC ACATCCACCCCTGTCTGCACATGAGTTGT TCTGACAACAGCG (SEQ ID NO: 53) DOCK5** NM_024940.6 6148-6247 GCTGTGCTTGAGACTTAGGTACTTTTCTC ACGTGGACACACTGATCCCATCCCATATT GCATCTTGGAAGAGATGGATATCAAGTAC ACTTTGGTAGCTG (SEQ ID NO: 54) DPEP1** NM_001128141.2 1033-1132 AGGCCAACCTGTCCCAAGTGGCCGACCAT CTGGATCACATCAAGGAGGTGGCAGGAGC CAGAGCCGTGGGTTTTGGTGGGGACTTTG ATGGTGTTCCAAG (SEQ ID NO: 55) DPM1* NM_003859.1 461-560 TATATGGCTGGGATTTGAAAAGAAAAATA ATCAGCCGTGGGGCCAATTTTTTAACTCA GATCTTGCTGAGACCAGGAGCATCTGATT TAACAGGAAGTTT (SEQ ID NO: 56) EIF6** NM_181466.2 435-534 GCTCAAGGTGGAAGTCTTCAGACAGACAG TGGCCGACCAGGTGCTAGTAGGAAGCTAC TGTGTCTTCAGCAATCAGGGAGGGCTGGT GCATCCCAAGACT (SEQ ID NO: 57) EN02** NM_001975.2 1855-1954 GGTTGGTGTGCTGAGGTGTTAGAGAGGGA CCATGTGTCACTTGTGCTTTGCTCTTGTC CCACGTGTCTTCCACTTTGCATATGAGCC GTGAACTGTGCAT (SEQ ID NO: 58) EPB41L4B NM_018424.3 3340-3439 TTTTTGGCTACTGCAAAAATCTATTCAGC AAGAAGGTATCAGCTGCATACCTTGCACA GTGGAGCTGACTACCTATAAACTCTCCCT AAGGCATTTGTTT (SEQ ID NO: 59) EREG** NM_001432.2 3627-3726 TGAGAACCAAAGCAACCACAAATGCATAA ATGCATAATTTATGGTCTTCAACCAAGGC CACATAATAACCCAGTTAACTTACTCTTT AACCAGGAATATT (SEQ ID NO: 60) ERRFIl NM_018948.3 2758-2857 GTGGCTGCTTCACTTAGATGCAGTGAGAC ACATAGTTGGTGTTCCGATTTTCACATCC TTCCATGTATTTATCTTGAAGAGATAAGC ACAGAAGAGAAGG (SEQ ID NO: 61) ESRP1 XM_005250992.2 3370-3469 TACTTTAACACCTTAAAGGGAGAAGCAAA CATTTCCTTCTTCAGCTGACTGGCAATGG CCCTTTAACTGCAATAGGAAG AAAGGTTTGTGTG (SEQ ID NO: 62) FABP5* NM_001444.1 101-200 GCTTTGATGAATACATGAAGGAGCTAGGA GTGGGAATAGCTTTGCGAAAAATGGGCGC AATGGCCAAGCCAGATTGTATCATCACTT GTGATGGTAAAAA (SEQ ID NO: 63) FAM105A** NM_019018.2 1352-1451 TTTTCAGTGATTTCCTCTGAAGCAGCTGC ACTGATACATTTGGGAGTTGGTGGCTTGA CTTTGTCCATAAGGGGCGTGGCCACTTCA CATGATGGCGGGC (SEQ ID NO: 64) FAM122B** NM_001166599.2 3776-3875 AAATTCAATGGTGGGATAGAATTAGGTCA GGAAATGGAAGTTGTTCCAATGGTGTGAG AACTAGGAGACAAGATGATTCACTTTATT ATTTAAACCAAGC (SEQ ID NO: 65) FAM3D** NM_138805.2 773-872 GGAAACTCTTCTCTGACTTGGGGAGTTCC TACGCAAAACAACTGGGCTTCCGGGACAG CTGGGTCTTCATAGGAGCCAAAGACCTCA GGGGTAAAAGCCC (SEQ ID NO: 66) FAM84A NM_145175.2 3017-3116 ACAGCTGTGATTTTGTTGGACAGCAAGTA TTATGGCCAAAGCCAGTTTCTTGGCATTT CAAAAATAATGCAATAAAAACTAGTTGAG GTTAGCTGAGGCT (SEQ ID NO: 67) FARP1* 227996_at.1  67-166 TCTCTGTTGCCGGATGCTGTCTAGGGCCT GTTAGTTGCTATTTCCTTGCCTCCGCTCC CCTTCCCACTAGCCTTCTAACTACCTTTT ATTCTCGGCTCCA (SEQ ID NO: 68) FBXO34** NM_152231.1 2732-2831 AAAAAGAAAAGTCAGGCACCCCACCTTAG ACCTCGTATGCTTGATCCTGTGAGATTGA TGTTTGTGGCTGGAGGTGGATTTCATGCC CTGTGGTGTTTAC (SEQ ID NO: 69) FITM2** 226805_at.1 135-234 TTCATCGTCTGTCAGGTGGAGATGAAGAG AAATGGTGCATGTAAAGTGCTCAGCATCT GTGCTTAGCAGGCCATAGTCTCCCTGCCT CCTTTTTCTTGAG (SEQ ID NO: 70) FOXA2** NM_021784.4 1775-1874 CCACACAGATACCCCACGTTCTATATAAG GAGGAAAACGGGAAAGAATATAAAGTTAA AAAAAAGCCTCCGGTTTCCACTACTGTGT AGACTCCTGCTTC (SEQ ID NO: 71) FRZB NM_001463.2 1813-1912 AAATAGGAGCTTAAGAAAGAACATTTTGC CTGATTGAGAAGCACAACTGAAACCAGTA GCCGCTGGGGTGTTAATGGTAGCATTCTT CTTTTGGCAATAC (SEQ ID NO: 72) FUT8** NM_004480.4 3411-3510 TGAGAAGATCGGAACAGCTCCTTACTCTG AGGAAGTTGATTCTTATTTGATGGTGGTA TTGTGACCACTGAATTCACTCCAGTCAAC AGATTCAGAATGA (SEQ ID NO: 73) GALNT5** 236129_at.1 278-377 AGCTGTCACGTTTGTGAAATCCCTCCAGA CTACATGCATGCTTACCTAACAGTTTGAA ATAGTATTGATCTACTGCTGGTAACCCTG CTTGATGGCAGCA (SEQ ID NO: 74) GALNT8* NM_017417.1 1537-1636 AAAATGTTTATCCACTCTTGAAGCCACTC CACACCATCGTGGGCTATGGAAGAATGAA AAACCTATTGGATGAAAATGTCTGCTTGG ATCAGGGACCCGT (SEQ ID NO: 75) GFPT1** NM_001244710.1 2615-2714 TGGTACTTGTTTCACCATACTTCATTCAG ACCAGTGAAAGAGTAGTGCATTTAATTGG AGTATCTAAAGCCAGTGGCAGTGTATGCT CATACTTGGACAG (SEQ ID NO: 76) GGH NM_003878.2  974-1073 ATGGAAGGATATAAGTATCCAGTATATGG TGTCCAGTGGCATCCAGAGAAAGCACCTT ATGAGTGGAAGAATTTGGATGGCATTTCC CATGCACCTAATG (SEQ ID NO: 77) GNG4** NM_001098721.1 4475-4574 GGTGTATACCCTACAGAAATGTGTACATG TGTTCATCCAGAGACATGCTCTAAATCTT CACAAAAACACTCTCCATAATAACCCCGA ACAGGAAAGCACC (SEQ ID NO: 78) GPCPD1 NM_019593.3 2731-2830 ACATGGGTTGACATGCACACAACACCATT TTCATTCAGTATGAACCTTGAGGCTGCTG CCATTTTTCCACTTAACCAAACCAGCCTG AAGGTGAACCTCG (SEQ ID NO: 79) GPR143** NM_000273.2 1285-1384 AATTGAAATTCACACTGCAAGTGAATCCT GCAACAAAAATGAGGGTGACCCTGCTCTC CCAACCCATGGAGACCTATGAAGGGGATG TGCTGGGGGTCCA (SEQ ID NO: 80) GPR153** NM_207370.2 3781-3880 GGGGTTTTGCTCTGTGTGTTTCATATCCA ACGGCAATACTTGCAGGGGGACAGAGTCC TCTAAATACTCCAATCCTGCGGTTTTTAC AAACATAAAGGGG (SEQ ID NO: 81) GRM8** NM_000845.2 2886-2985 GTGGTGACAGCTGCCACCATGCAAAGCAA ACTGATCCAAAAAGGAAATGACAGACCAA ATGGCGAGGTGAAAAGTGAACTCTGTGAG AGTCTTGAAACCA (SEQ ID NO: 82) GTF2A2 NM_004492.1 246-345 AGTTCTACTTCAGTTTGATAAGGCTATAA ATGCAGCACTGGCTCAGAGGGTCAGGAAC AGAGTCAATTTCAGGGGCTCTCTAAATAC GTACAGATTCTGC (SEQ ID NO: 83) GUCY2C NM_004963.3 3250-3349 AGGCAGCAGGGATAAGAAGCCAAAAACCC AGACGGGTAGCCAGCTATAAAAAAGGCAC TCTGGAATACTTGCAGCTGAATACCACAG ACAAGGAGAGCAC (SEQ ID NO: 84) GYG2 NM_001184704.1 2141-2240 CTCTTGGCTTGGTCTCTACCCTCACTACC TCAGTTCTCAATAACTTAGTGAATCACTG CCCTCCTCAAAGCCATTTCCACTCAGCTC TTTCCAGAGAATT (SEQ ID NO: 85) HEPH** NM_001130860.3 4041-4140 GTATCCTTCTCACAAAGTAGAGACCAAGA GAAAAACTCATTGATTGGGTTTCTACTTC TTTCAAGGACTCAGGAAATTTCACTTTGA ACTGAGGCCAAGT (SEQ ID NO: 86) HES2** NMO19089.4 3878-3977 TCCACGTGAGTGAGGATAAAGACTGGGCT GCCAAGGAGGACTCCTCATAAACATTGAC AAATTGCTCTGCCCCGCCTGTGATCCCAG ACGACTCCTGCAG (SEQ ID NO: 87) HLA-E NM_005516.5 2124-2223 ACTGGTGGCTTTATAAGAAGAGGAAAAGA GAACTGAGCTAGCATGCCCAGCCCACAGA GAGCCTCCACTAGAGTGATGCTAAGTGGA AATGTGAGGTGCA (SEQ ID NO: 88) HLX NM_021958.3 1721-1820 GGGAGTGGTGGGAGCAGCGGCGGCGGCGG CAATAGTTTCAGCTTCAGCAGCGCCAGCA GTCTTAGTAGCAGCAGCACCAGTGCGGGT TGCGCCAGCAGCC (SEQ ID NO: 89) HOXD11 NM_021192.2 1069-1168 ACCAGCCTGCTCTCCGCAGGCCCACTGTC CTTGGGTTTAATGACGTCTCTTCTCTGTG GAACTTCACGATTCCTTCCCACGGTCAAC TCGGGACCTCCCA (SEQ ID NO: 90) HSPA4L** NM_014278.2 2745-2844 AATCCTCTGGAGAGATGGAAGTGGACTAA GTCTTAATTTTACCTTCACATTAATTCAA ACCGTGCAAGTAACCACGGGGTCCATCTT TTACATCTGGTAC (SEQ ID NO: 91) HSPA6 NM_002155.3 2019-2118 GACAGAGTGGCTGCCAAAAACTCGCTGGA GGCCCATGTCTTCCATGTGAAAGGTTCTT TGCAAGAGGAAAGCCTTAGGGACAAGATT CCCGAAGAGGACA (SEQ ID NO: 92) HUNK** NM_014586.1 4727-4826 GCAGGGTGTATACCTGCGCATTGGGAACT TGCTGGAACCCCTGATGCATTTTCCTTGA GAGCAGGGGTACTTCCGCCTTGCCGTTAG CTTGTGGAGAACG (SEQ ID NO: 93) IFIT3 NM_001031683.2 2136-2235 TGGTAGCAATAAATGCTTCAGGCCCACAT GATGCTGATTAGTTCTCAGTTTTCATTCA GTTCACAATATAACCACCATTCCTGCCCT CCCTGCCAAGGGT (SEQ ID NO: 94) ILDR1 NM_001199800.1 2106-2205 TGCATATTTATATAATCTCTGACTTGTAA TGGACCCTGACTGGAATGTGATCCCTCAG GAACTTAGTAGCCTGAGTCTTTCAGTAGA CTACACTGCCCAG (SEQ ID NO: 95) IMMP2L NM_032549.3  985-1084 CCTGAGTTGCTGGCATTGGGAGGCCAGTT ACTGGAAAGGAATGGAAAAAAGAAGCCTC CAAAAGGGAAAAACTTCTGACAATATGAT GCTGTGCGAGAAA (SEQ ID NO: 96) JADE3 NM_014735.3 4564-4663 GTAGCCTTTGTCCCTTCATGCCTTTCAAT TCTGAGTGGGAGGAAAAGCAAACATCAAA ACAGTGCTTCAGCCAAATTCCATATGTAA TGCCATTGGGAGA (SEQ ID NO: 97) KCNK1 NM_002245.3 1119-1218 AAATGAGCCTTTTGTGGCCACCCAGTCAT CTGCCTGCGTGGATGGCCCTGCAAACCAT TGAGCGTAGGATTTGTTGCATTATGCTAG AGCACCAGGGTCA (SEQ ID NO: 98) KCTD1 NM_001258222.1 1095-1194 CAGCCGCAGTTGGTGCTGTGATGGCCGTG AAGTGTCCTGGGCCTCCCGAGGCCTCTGA CAAATAAACAAGCCATGAGTGGTGAGGAC ACAGTCTCCTTAC (SEQ ID NO: 99) KIF2C NM_006845.3 2634-2733 GGGTTGTCCTGGCTCTGGGGAGAGAGACG GAGCCTTTAGTACAGCTATCTGCTGGCTC TAAACCTTCTACGCCTTTGGGCCGAGCAC TGAATGTCTTGTA (SEQ ID NO: 100) KLK1 NM_002257.3 498-597 TTTGGCTTCCGGCTGGGGCAGCATCGAAC CAGAGAATTTCTCATTTCCAGATGATCTC CAGTGTGTGGACCTCAAAATCCTGCCTAA TGATGAGTGCAAA (SEQ ID NO: 101) LDLRAD3 NM_001304263.1 3026-3125 AGCCAGAATGTGTTAGAACTCTGGCTGAA CATTTCATCTCCTGTGAGTCAGAAGGGCT TTATTTCTCCCTTTGATGGGGCCCCTTCT TCTTTCTGGTGCT (SEQ ID NO: 102) LEFTY1** NM_020997.3 1181-1280 GGCGCCTAGTGTAGCCATCGAGGGACTTG ACTTGTGTGTGTTTCTGAAGTGTTCGAGG GTACCAGGAGAGCTGGCGATGACTGAACT GCTGATGGACAAA (SEQ ID NO:103) LMO4 NM_006769.3 1438-1537 TTGGTGTATTAAAATGACTGAATATGAAC ATTAAGGACTCCATGAACCTGGGCTAATG GGAGACTGTAGAGAAAATGAAAAAAGATC CACCAGAGGACAT (SEQ ID NO: 104) LNX2*¹ XM_011534995.1 3798-3897 AAGATACAGCAACAATCATTGCTACTGAC TTGTTCAACCCCTTAGTTACACTGTATGA TCAACATATAACAAGATACAGTGGAATGG CCCATACAGTATA (SEQ ID NO: 105) LRRC16A** NM_001173977.1 4823-4922 TTGGAGATAAATGAAATAACTGGACACAC ACTCACACAAGTAACACCACAGCAGACCT CGGAGTACTGCTAAGTGTACCTGTGTCAA ATCCGCACAGGAC (SEQ ID NO: 106) MAGED1 NM_001005333.1 2523-2622 ATTTTGGAGATCCCTGGTCCAGAATTCCA TTTACCTTCTGGGCCAGATACCACCAGAA TGCCCGCTCCAGATTCCCTCAGACCTTTG CCGGTCCCATTAT (SEQ ID NO: 107) MAP2K6 NM_002758.3 1281-1380 TCTTGGAGACTAAAAAGCAGTGGACTTAA TCGGTTGACCCTACTGTGGATTGGTGGGT TTCGGGGTGAAGCAAGTTCACTACAGCAT CAATAGAAAGTCA (SEQ ID NO: 108) MAPRE1 NM_012325.2 1983-2082 AGTGAAGGCCATCGTTACCTTGGGATCTG CCAGGCTGGGGTGTTTTCGGTATCTGCTG TTCACAGCTCTCCACTGTAATCCGAATAC TTTGCCAGTGCAC (SEQ ID NO: 109) MLLT3** NM_001286691.1 3370-3469 TTTACTAGAAATTCAGCCGAAAAGAAGAG AAATGAAGAAATACTTCTGGATCCAAAGG TTCGTCACTGGATCAGCCTTAAGAAAGTC TCTATGTGTGCTA (SEQ ID NO: 110) MLPH NM_001042467.2 1966-2065 GACAGGACAGAGAGACAGAGCAGCCCTGC ACTGTTTTCCCTCCACCACAGCCATCCTG TCCCTCATTGGCTCTGTGCTTTCCACTAT ACACAGTCACCGT (SEQ ID NO: 111) MPP1 NM_001166460.1 1596-1695 AGTTTTGTGTCAGCTTCCAGCTCTCTGCA GCTATCCTAATTCAGCCAGTAAGGTTCAG TCTTCTTGCTCAGGCTCCTGAAGGGTTGA TTCTCCTGATAGA (SEQ ID NO: 112) MRAP2** XM_011535401.1 1557-1656 GGTTGTGCTGAAACAGCTCTTCTGAGAAC TTCCAACCACCCATGCTCTAACCTGGAGA CAGCCATCCCCTGCCTCAGAATAAGTACC AATTCGTAGTACA (SEQ ID NO: 113) MT2A NM_005953.3 226-325 CAGGGCTGCATCTGCAAAGGGGCGTCGGA CAAGTGCAGCTGCTGCGCCTGATGCTGGG ACAGCCCCGCTCCCAGATGTAAAGAACGC GACTTCCACAAAC (SEQ ID NO: 114) MYRIP NM_001284423.1 4445-4544 GACAAAAATGTGTACTGTGTAAGCCTTGC AAACAAAAAACAACAAAAAAGAAGCAGCA GCAGCAGCCTGCTGTGTGGCATCTGAACT TTTATAAAGGTTT (SEQ ID NO: 115) NCAPH* NM_015341.3 2399-2498 GCTTATACCCAGGCTGTAGCCAACTACCA ACGTGCCTGTTTGTTTGTTGCTCTTTCCT TCTCTCCATCATAGTCTGGGTGCCAGCGC CCTGAAGCTCCGT (SEQ ID NO: 116) NDFIP2 NM_001161407.1 2177-2276 ACTCATTTTCAAGTTATGGAAATGTGTTT GTGGCATATAGGACTGTGGGGTCTGTGTG TGTAGTGAGAGTGTGTAGCCACTATTATA ACTGGAATTTAAT (SEQ ID NO: 117) NEDD9** NM_001142393.1 4061-4160 TCCATAGTCTGTCTCCTCACATCTGTTAG TATTGACACAGCACAGACACCACAAGCCA TCAGGTTCTTCATGGGGCAGGTGAAATAC TTCTACCCCATGG (SEQ ID NO: 118) NOL4L** NM_080616.4 5608-5707 CCACCTCCGGGGAGGGGCACAGGGCTCCA GATAGTAAGCACTTAAGGCAAACAGTGGA TGGCACCAACTTTTAAAGGTGACTCTATT AATGGCTTCACCT (SEQ ID NO: 119) NR1I2 NM_003889.3 4017-4116 ATGAGTCTGTAGGAGCAAGGGCACAAACT GCAGCTGTGAGTGCGTGTGTGTGATTTGG TGTAGGTAGGTCTGTTTGCCACTTGATGG GGCCTGGGTTTGT (SEQ ID NO: 120) NUCB2 NM_005013.2  999-1098 TTACATGATGTCAATAGTGATGGATTCCT GGATGAACAAGAATTAGAAGCCCTATTTA CTAAAGAGTTGGAGAAAGTATATGACCCT AAAAATGAAGAGG (SEQ ID NO: 121) OSTM1 NM_014028.3 2541-2640 ACTTTCTCTGATCTGCTGTGATCCACTGA AAATGTGCTGGGGTTTGTTCTGCTGTCAC TGTTTATGCTGCTGGAACTTAGCACTGTC TTGATTTGAAGCA (SEQ ID NO: 122) PAK6 NM_001276718.1 3753-3852 GGCCAGAGACAGGAATGTAAGGATTGGCA ACTGTGTTACCTTTCAAGTTTATCTCAAT AACCAGGTCATCAGGGACCCATTGTTCTC TTCAGAACCCTAT (SEQ ID NO: 123) PALB2 NM_024675.3 3551-3650 GTTCCTGGAAGGTGACGTGAAAGATCACT GTGCAGCAGCAATCTTGACTTCTGGAACA ATTGCCATTTGGGACTTACTTCTCGGTCA GTGTACTGCCCTC (SEQ ID NO: 124) PALLD NM_001166108.1 5451-5550 GTAAGAACACCAACCAACCAAGGTTTAAG TGATTAATAGGCTTGAGCACCGGGTGGCA GATGTTCTATGCAGTGTGGTTCAAGTTTC TTTGACCGCACTT (SEQ ID NO: 125) PBX1 NM_001204961.1 6321-6420 TACCTGAACACTTGTACTCTTGAAGTCAC AACAAAATAATGATGAGCTTTTCACATCA CCTTTATGGTTTCAATCCCTAGCTCAAAG CTTCCTGGAATCT (SEQ ID NO: 126) PCMTD2** NM_001104925.1 2361-2460 GCCTTGTGTGTGGAGAGCTTTCTATCTTA CCAAGTGGTAGGGCTAAAAGAACAACAGC CTTTTTGGTAGTCACATAGCAGAATGATC AGAGTTACATTGC (SEQ ID NO: 127) PCP4 NM_006198.2 156-255 AGTTCAAGAAGAATTTGACATTGACATGG ATGCACCAGAGACAGAACGTGCAGCGGTG GCCATTCAGTCTCAGTTCAGAAAATTCCA GAAGAAGAAGGCT (SEQ ID NO: 128) PDP1 NM_001161779.1 3871-3970 TGATCAAGATAGTAGTATTATTACACAAG AAACTTGGTCTGCAGTCTGGAAGCTTGTC TGCTCTATAGAAATGAAAATGCAGCATGA AGTTGACATTGTG (SEQ ID NO: 129) PDZD2 NM_178140.2 11011-11110 ACCTCTGGCTTACCACATACACTATGCTA AAGTCATCAGCCACTGCTACTACATCTTG CCAGAAGGTTTCCCTCGCCAACAAACAGT TGAAATTTAAGGG (SEQ ID NO: 130) PDZK1IP1 NM_005764.3 237-336 CTGGGGAACCTTCAGCCCTGGATGCAGGG CCTTATCGCGGTGGCCGTGTTCCTGGTCC TCGTTGCAATCGCCTTTGCAGTCAACCAC TTCTGGTGCCAGG (SEQ ID NO: 131) PIGR NM_002644.3 2303-2402 ACATCCCTCGGAGGAAAAGAAGAGTTTGT TGCCACCACTGAGAGCACCACAGAGACCA AAGAACCCAAGAAGGCAAAAAGGTCATCC AAGGAGGAAGCCG (SEQ ID NO: 132) PIGU NM_080476.4 1317-1416 TGGCACCTCTGGATTTATGCAGGAAGTGC CAACTCTAATTTCTTTTATGCCATCACAC TGACCTTCAACGTTGGGCAGATCCTGCTC ATCTCTGATTACT (SEQ ID NO: 133) PITX2 NM_000325.5 1707-1806 TAAAGAAAGGGAGAGAAAGAGAAGCTATA TAGAGAAAAGGAAACCACTGAATCAAAGA GAGAGCTCCTTTGATTTCAAAGGGATGTC CTCAGTGTCTGAC (SEQ ID NO: 134) PLCB1** NMO15192.3 6569-6668 ATAGAAAGTAGAGCTGTGTATTAAATTAG ACTGTGTCTCTCTGATACCTTTACACTAC TGAGAATAGCATGGTTTTGGCCATGTAAA CCAATTTTCAAAG (SEQ ID NO: 135) PLCH1* NM_014996.2 5794-5893 CTGATTGAATTACAGACTGCGAACAACGG CTTTCAGAATGAGGGACTTCCATCAGACT CTAATGATAATAGTAGCACAAATTGAAAA CTTCCCCAAAGCT (SEQ ID NO: 136) PNP NM_000270.2 1151-1250 TTTCTTCTACCAGACCCTTCTGGTGCCAG ATCCTCTTCTCAAAGCTGGGATTACAGGT GTGAGCATAGTGAGACCTTGGCGCTACAA AATAAAGCTGTTC (SEQ ID NO: 137) POLD3 NM_006591.1 2901-3000 AGCAACCAAGCATGAACTTGATTAAGACC AGAAGTTTGGGAGATGAGTCCTGGCATTA TGTCTAGGACTAAAGCAGTGGCTTTGTAT AGCAAGCTGAGTA (SEQ ID NO: 138) POP1** NM_001145860.1 4129-4228 CAAGGAGCCCTTTGTAGGACCAGTGTTCT TAGTAGCGCGCTTTGGGCAGTGTGGCTGT GTAGTGCATAGCTACCTCTGCAAGGTGAT AACTAAGCCGGCA (SEQ ID NO: 139) PPP1R14C** NM_030949.2 1581-1680 CTTCCTTAGAAATAGGTTCTGGTAGCTTC TGTGCCTGGGTAGTATCAGACCAGTGGGA GTAAACCGAGTGTTAAGTGTCAAGGTGAG AAAGCCTCACATT (SEQ ID NO: 140) PPP1R14D NM_001130143.1 721-820 GAAATGTGGACAAGGAGGGACATTTGCAT ACTCCTACTGTCTGTGTGGTCACAGCTAG TTTCTGTCAGCTGGGCTCTCTGGGAGAAA GCTGGCTGTTGTC (SEQ ID NO: 141) PPP1R3D* NM_006242.3 2942-3041 TCACTACATTAACATGCAAGAGAGAGAGA AGCCTTGTTACATTTCCTGCTATTTAACA AACTGTCCAATTAGGTCAGCAAGCCTGTT AGGGCCTTCACTG (SEQ ID NO: 142) PPP3CA NM_000944.4 3632-3731 GTATAAGTGCCCAAGTAATTCACTACAGC CTAAAGCCTTGCCTTTGTAATTTGACTTC TGACATGTTGGCAATCAAAGCATGCACTT GTAACAATGAAAA (SEQ ID NO: 143) PRAP1 NM_145202.4  79-178 ATGAGGAGGCTCCTCCTGGTCACCAGCCT GGTGGTTGTGCTGCTGTGGGAGGCAGGTG CAGTCCCAGCACCCAAGGTCCCTATCAAG ATGCAAGTCAAAC (SEQ ID NO: 144) PRC1 NM_199413.2 2803-2902 ATTGGGAGTCTGTTTGTTCCAATGGGTTG AGCTGTCTTTGTCGTGGAGATCTGGAACT TTGCACATGTCACTACTGGGGAGGTGTTC CTGCTCTAGCTTC (SEQ ID NO: 145) PRDX5 NM_012094.4 601-700 GGAAGGAGACAGACTTATTACTAGATGAT TCGCTGGTGTCCATCTTTGGGAATCGACG TCTCAAGAGGTTCTCCATGGTGGTACAGG ATGGCATAGTGAA (SEQ ID NO: 146) PRLR NM_000949.5 11449-11548 AAGAAAAGGAAAGAGGATGTGGGTCAAAT AAAACACCGCATGGATGTTGATTGGTGAA TACTGGTGTAAGAAAAGGGAGCTCAGGAA TTTTTATTACTGT (SEQ ID NO: 147) PRR15* NM_175887.2 1421-1520 AGCTTGAACTCTGTAGCCTCTCAAATGAA GAAGGTGGCTGTTATTTAGGACTCTGTGG AAAGCAAATCACAGTGCTGTTTTTAATGC CTGAGAAATGCAC (SEQ ID NO: 148) PTPRO NM_002848.3 4793-4892 TACTGTCCAAGTTCTTTCTCAAGAAACCA CATCTGGTTCAGAAGAGTGTCAAGTTGGA CTCTTTGAACTCTGTTGCTGTCTGAGCAA TCGTGGTGCCTAG (SEQ ID NO: 149) QPRT** 242414_at.1 138-237 AAAAGTTAGAAAACAAAACAAAACAGAAG TAAGATAAATAGCCAGAAGACCTTGGCGA CACCACCCGGCCCTGGTAGTT GTAACAATAATAA (SEQ ID NO: 150) RAB27B NM_004163.4 787-886 ATGAAGCGAATGGAACAGTGTGTGGAGAA GACACAAATCCCTGATACTGTCAATGGTG GAAATTCTGGAAACTTGGATGGGGAAAAG CCACCAGAGAAGA (SEQ ID NO: 151) RAMP1 NM_005855.3 370-469 CTGGGCTGCTTCTGGCCCAATGCAGAGGT GGACAGGTTCTTCCTGGCAGTGCATGGCC GCTACTTCAGGAGCTGCCCCATCTCAGGC AGGGCCGTGCGGG (SEQ ID NO: 152) RAN NM_001300796.1 1765-1864 GTAGGGCAGCACAGCAGAGCAGGACATGG ATGAAATACTAGGAATATGCACAGTGGGG CAGTGTGGGGGCTTCTCAGTAATGGAGAA CAGTTGGTGAAAC (SEQ ID NO: 153) RAP1GAP** NM_001145657.1 2925-3024 CTCTGGGCATGTCTGCTACAAATGGACAA GATTATTTCAGAGGTCACTGAAGACTGTG ATTACATGCACCTGCCTTAGAAGGTAGGA TTTTCTTCCCAGG (SEQ ID NO: 154) RARRES3 NM_004585.3 442-541 CCCGCTGTAAACAGGTGGAAAAGGCCAAG GTTGAAGTTGGTGTGGCCACGGCGCTTGG AATCCTGGTTGTTGCTGGATGCTCTTTTG CGATTAGGAGATA (SEQ ID NO: 155) RBMS1 NM_002897.4 1629-1728 TCCATATACCTTTCAACCTAATAAGTAAC TGTGAGATGTACAGAAAGGTGTTCTTACA TGAAGAAGGGTGTGAAGGCTGAACAATCA TGGATTTTTCTGA (SEQ ID NO: 156) REG4** NM_001159352.1 870-969 AACGAATGCAACAAGCGCCAACACTTCCT GTGCAAGTACCGACCATAGAGCAAGAATC AAGATTCTGCTAACTCCTGCACAGCCCCG TCCTCTTCCTTTC (SEQ ID NO: 157) RETNLB* NM_032579.2 189-288 ATGGATAAGAAGATCAAGGATGTTCTCAA CAGTCTAGAGTACAGTCCCTCTCCTATAA GCAAGAAGCTCTCGTGTGCTAGTGTCAAA AGCCAAGGCAGAC (SEQ ID NO: 158) RNF183 NM_145051.3 655-754 CTCCATCTTTTGGACCAAGCAGTTCCTTT GGGGTGTGGGGTGAGTGCTGTTCCCAGAC AAGAAACCAAACCTTTTTCGGTTGCTGCT GGGTATGGTGACT (SEQ ID NO: 159) RNF43** NM_001305544.1 3814-3913 GAGTCCCAGAGAGGTAGAAAGGAGGAATT TCTGCTGGACTTTATCTGGGCAGAGGAAG GATGGAATGAAGGTAGAAAAGGCAGAATT ACAGCTGAGCGGG (SEQ ID NO: 160) SAMD5** NM_001030060.2 5961-6060 TGCCCTGTTCCCCAAGCTTGTCAATGTTT AGAGATACTATTCGGGTTGCTAAAGCCAT TATTCATAGAAAATTTCTGCCCCTACAGA AGTGTGTGCATGG (SEQ ID NO: 161) SEMA5A NM_003966.2 11231-11330 GCTTCCTGAGAGCTGTCTAGGTCTGTATC CCAGATTGTTGCTTAATGACATCTGACAG ATGCATTGTTTTCTGAAATCAGCTTAAGA CACCAATTGTGGC (SEQ ID NO: 162) SERPINB1** NM_030666.2 817-916 GGACTAAACCTGAGAATCTCGATTTCATT GAAGTTAATGTCAGCTTGCCCAGGTTCAA ACTGGAAGAGAGTTACACTCTCAACTCCG ACCTCGCCCGCCT (SEQ ID NO: 163) SESN1** NM_001199933.1 2328-2427 GGATCCTGACACTGGAGGGCAGCTGTCTT GTGCATTACTTGTGTTTCCAGCACCAAAG TTGTGGGACATGTTGCTGTAGACTGCTGC GCAGTCCTGGGTG (SEQ ID NO: 164) SHROOM4** NM_020717.3 7387-7486 GGAGAGGTGAAAAGATAAAAAGCCTCCTT CAAGGTTAGGTTCAGGTTCTGTTTTCCAT TTAACCTCATGTGCCATAAAGCTGCCCAG GCACACCAGAGCC (SEQ ID NO: 165) SLC25A37** NM_016612.2 1724-1823 TTTTAAGAGGGTTGAATTCTTCCATCAGG TGAACGAAAAAGGCAACAAAGTAATAAAT CAGTGAATGTGGCCGGCAGCTGTGTTTAG CCCCTCCAGATGG (SEQ ID NO: 166) SLC30A2 NM_001004434.1 2298-2397 CCGACTCATCGAGACAACATGCCCAGTTA TCAGGGAGTCCTGTGTCACAAGGTCTGTC TCTGCCATTGTAAGCAAGTGCCTTGGGCG AGCTGGCCTCTGC (SEQ ID NO: 167) SLC4A11** NM_032034.2 2956-3055 CCAGGGTGGGTGGGACTGAGCAGGATGGA TTTTCTTTTGATAAAAGAGTCGATGCCTG AAAGAGAAACCATTTCCTTGATTGTGTAA GGAACTTGCTGGA (SEQ ID NO: 168) SLC9A2 NM_003048.3 2631-2730 TTGTCACTCTGAAACCTGATGCAACAGTG GAATCCATGTAAAACTCTCTGTGCATCTA AATACTTCTGGAGGGCGACAGATTCATGC CACGGATAAATGA (SEQ ID NO: 169) SLCO1B3** NM_019844.3 2596-2695 GGTAGTTGTAACTGCTAATAAAACCAGTG ACTAGAATATAAGGGAGGTAAAAAGGACA AGATAGATTAATAGCCTAAATAAAGAGAA AAGCCTGATGCCT (SEQ ID NO: 170) SLCO2B1 NM_001145211.2 3194-3293 GCCTGGGTCTGTGTCACCTGGGGCAGTGT GGATAATGTTTAGTTCTGTGACACTGTTT TTTGGGGGTGGCACCTGGTTCTCCGATGC CTGGGCTGGTGTC (SEQ ID NO: 171) SOCS6 NM_004232.3 1883-1982 CCAAACAAAATGAAGGATTATTTACAGGA GAAGCACTACTGAAAGATTGAGAACCCTG CATCTTGCACTTTGGGAATAAGAACAAGA GATTGAAATACAG (SEQ ID NO: 172) SPIRE2** NM_032451.1 1817-1916 CTCTTCTGCAAGAGAGCCGTCTGCACTTC CTGTAGCATAAAGATGAAGATGCCTTCTA AGAAATTTGGACACATCCCTGTCTACACA CTGGGCTTTGAGA (SEQ ID NO: 173) SRPK1 NM_003137.4 4080-4179 GGAAATGCTTCTCCACCAAATAAGGGCTT TTTCCCCTATTTAAGGAGCCAGATGGATT GAAAGATGTGGAAATAGGCAGCTGTAGAT CTTGATCTTCCAG (SEQ ID NO: 174) ST6GALNAC1 NM_018414.3 2104-2203 AACACTTGAACCATGGACAAGACTCTCTC AAGATGGCAAATGGCTAATTGAGGTTCTG AAGTTCTTCAGTACATTGCTGTAGGTCCT GAGGCCAGGGATT (SEQ ID NO: 175) STAT2 NM_005419.3 4407-4506 ATGTTCTCCTGATGTAGCTTGAGATATAA AGGAAAGGCCCTGCACAGGTGGCTGTTTC TTGTCTGTTATGTCAGAGGAACAGTCCTG TTCAGAAAGGGGC (SEQ ID NO: 176) TC2N NM_152332.4 2206-2305 ACCTGGTATATCAAGTCTCTGTTAGTACT ATTGGCATTTAGAAGACTTTACCATTATT TCAGTGCTAGGCATTATTGATTAGGTCTT GGCTCCACTGTTT (SEQ ID NO: 177) TCN1** NM_001062.3 1006-1105 ACAAAGACTCTTCTTGCGTCTCTGCTTCA GGTAACTTCAACATCTCCGCTGATGAGCC TATAACTGTGACACCTCCTGACTCACAAT CATATATCTCCGT (SEQ ID NO: 178) TFAP2A** NM_003220.2 2790-2889 TGGGACCACCTGGTATTCTGTATTTTCAC TGGCCATATTGGAAGCAGTTCTAGTTGCA TTGTATTGAGTTGTGCTGGCAGTAGTTTC CATGCCTGTCAAT (SEQ ID NO: 179) TMEM61 NM_182532.1 881-980 GTTCAGACTGCACGGGGAGGAAGTTAAAG GCTCCTAGCAGGTCCTGAATCCAGAGACA AAAATGCTGTGCCTTCTCCAGAGTCTTAT GCAGTGCCTGGGA (SEQ ID NO: 180) TMEM64** NM_001146273.1 2531-2630 TGAGTAACAGTAAAGTTCATTTATATGTC CATACCTAGAAGACCAGTGCAAATACTTT GAGAGCACCTGGGTCTACAGGACATAATT GGCATCTAAATCC (SEQ ID NO: 181) TNFRSF11A NM_001270949.1 1941-2040 CAGTGTGTGTTCATTGTAAACACTTTTGG GAAAGGGCTAAACATGTGAGGCCTGGAGA TAGTTGCTAAGTTGCTAGGAACATGTGGT GGGACTTTCATAT (SEQ ID NO: 182) TNNC2* NM_003279.2 387-486 AGACGGCTACATCGACCCGGAGGAGCTGG CTGAGATTTTCAGGGCCTCCGGGGAGCAC GTGACGGACGAGGAGATCGAATCTCTGAT GAAAGACGGCGAC (SEQ ID NO: 183) TN54* XM_005257744.1 3979-4078 CACCCAGCCAGTGGTCGAGCACTGCCCCG CCGCCAAAGTCTGCAGAATGTGAGATGAG GTTCTCAAGGTCACAGGCCCCAGTCCCAG CCTGGGGGCTGGC (SEQ ID NO: 184) TOMM34 NM_006809.4 1437-1536 AGACCCCAACTCACTGCAGTTCATCTGAA CAACCTGAGCTCCTGGGCCGGGGTGGAAG GAGGGGGATAAACCTAAGGCCCTGATCCA AAGCAGCCTGTTG (SEQ ID NO: 185) TRIM7 NM_033342.2 579-678 AGGAGGCCAAGGAGCTCTTGGAGTCCAGG CTGAGGGTCTTGAAGAAGGAACTGGAGGA CTGTGAGGTGTTCCGGTCCACGGAAAAGA AGGAGAGCAAGGA (SEQ ID NO: 186) TRNP1* NM_001013642.2 1324-1423 ATTTTAATAGATGTCATTGCTTCAAGTCT AACGGCGCCGGGAGGCCTGTTTGAGGGAA AACATTAGTTTGAAAAATCCCCGTTCCCT TCATCCACTGCCC (SEQ ID NO: 187) TSPAN6** NM_001278740.1 1792-1891 TTAATTTCAGTCAGTCAATAGATGGCATC CCTCATCAGGGTTGCCAGATGGTGATAAC AGTGTAAGGCCTTGGGTCTAAGGCATCCA CGACTGGAAGGGA (SEQ ID NO: 188) UGT8* XM_006714303.2 3333-3432 TACCTAATGTCATTCACTAACATGGAAGA GTTGTGAAAATTCTAGAGTGCTGTAAATC CTTGGCATACACTATGACAAACAACTTCA TTACTCTCCCACC (SEQ ID NO: 189) UPF3A* NM_023011.3 2016-2115 AGAGTTCACACTATATAAAACCCAACAGC TTCAACTATTGCCCTTTCAACAGTTTTGC CACTGACCGGATAGAAACGGTTTCAGTCT CTGGATGGATGTG (SEQ ID NO: 190) USP14* NM_005151.3 2166-2265 GGAAGTGTTGCTCTCATTGTGTGACTCAG TGCTGCTGTCCATCCCATGGAAACATGGG CACAATCAAGTATTTGTCCAGCCTATTGC AGGCTTTTCCTGA (SEQ ID NO: 191) UTP15 NM_032175.2 1666-1765 AGAAACCTTGGGGATGATGGATATGCTTT TTGCCACCATGAGAAGGAAGGAAGGCACT TCTGTGTTGGAACACACATCTGATGGATT TCCAGAGAATAAG (SEQ ID NO: 192) VAV3** NM_001079874.1 2681-2780 GAATGTTTTGTCTGTTGCCGTCAGCCGAA CTTTGTTATGGAGGGAGCAGCCTCACACA AGCAGAAACACTCCTGTGGATGGTATTGT AGCATGTATTGTT (SEQ ID NO: 193) WFDC2 NM_006103.3 165-264 CGCAAGAGTGCGTCTCGGACAGCGAATGC GCCGACAACCTCAAGTGCTGCAGCGCGGG CTGTGCCACCTTCTGCTCTCTGCCCAATG ATAAGGAGGGTTC (SEQ ID NO: 194) XBP1 NM_005080.3 1595-1694 TGCCTCCAGTTTTAGGTCCTTTAGTTTGC TTCTGTAAGCAACGGGAACACCTGCTGAG GGGGCTCTTTCCCTCATGTATACTTCAAG TAAGATCAAGAAT (SEQ ID NO: 195) ZCCHC24** NM_153367.3 4394-4493 CAAAGCCTGGCCTCGCCGCTCGGGAGCTT TGCCATCTGAGCCACGCCTCCTCCAGGCC ATGCTCCTTGAACTTGGAAATGTCAACCG GAGCCCTTACACC (SEQ ID NO: 196) ZDHHC23** NM_173570.3 3171-3270 GGTTAGTGAAGACAAATGTCTTAAGAGGC TGCGATGTCTAGGTTGGGCTTGTGACTTC TTAGTGGCCTAGCCTTCTTGATGGCACCT TGAAAGTGAACTT (SEQ ID NO: 197) ZG16B** NM_145252.2 405-504 AAGTCACCCTGCAGCCAGGCGAATACATC ACAAAAGTCTTTGTCGCCTTCCAAGCTTT CCTCCGGGGTATGGTCATGTACACCAGCA AGGACCGCTATTT (SEQ ID NO: 198) ZNF415** NM_001136038.2 1911-2010 TAATCCATACTGGAAAGAAACCTTACAAA TGTAGTGATTGTGGGAAGTCCTTTAGTGT GCGCCCAAACCTCTTCAGACATCAAATTA TCCATACTAAGGA (SEQ ID NO: 199) ZSCAN18** NM_001145542.1 2231-2330 GCGTGTGTTTACCTATATGGAGTAGCTCG CAGAGATCACAGAAATGCTTGCAGCCTAA GGCAGGGTTTTCAGACCGTGGGTCCCAGC CCATTTAGTAAAA (SEQ ID NO: 200) ZWINT* NM_007057.3 1125-1224 GGACTGGTTTGAACACAGGGTGTGCAGAT GGGGAGGGGGTACTGGCCTTGGGCCTCCT ATGATGCAGACATGGTGAATTTAATTCAA GGAGGAGGAGAAT (SEQ ID NO: 201) ¹LNX2 is included in the 100-gene classifier but not the 200-gene classifier

III. Methods of Treating Colorectal Cancer

Colorectal cancer (CRC), also known as bowel cancer and colon cancer, is a cancer characterized by neoplasia in the colon, rectum, or vermiform appendix. The symptoms of colorectal cancer depend on the location of tumor in the bowel, and whether it has spread elsewhere in the body (metastasis). The classic warning signs include: worsening constipation, blood in the stool, decrease in stool caliber (thickness), loss of appetite, loss of weight, and nausea or vomiting in someone over 50 years old. While rectal bleeding or anemia are high-risk features in those over the age of 50, other commonly described symptoms including weight loss and change in bowel habit are typically only concerning if associated with bleeding.

Colorectal cancer is the third most commonly diagnosed cancer in the world, but it is more common in developed countries. More than half of the people who die of colorectal cancer live in a developed region of the world.

Most colorectal cancers are due to old age and lifestyle factors with only a small number of cases due to underlying genetic disorders. Greater than 75%-95% of colorectal cancer occurs in people with little or no genetic risk. Some non-genetic risk factors include diet, obesity, smoking, and lack of physical activity. Dietary factors that increase the risk include red and processed meat as well as alcohol. Another risk factor is inflammatory bowel disease, which includes Crohn's disease and ulcerative colitis. Some of the inherited genetic disorders that can cause colorectal cancer include familial adenomatous polyposis and hereditary non-polyposis colon cancer; however, these represent less than 5% of cases. It typically starts as a benign tumor, often in the form of a polyp, which over time becomes cancerous.

Colorectal cancer starts in the lining of the bowel. If left untreated, the cancer can grow into the muscle layers underneath, and then through the bowel wall. Most begin as a small growth on the bowel wall: a colorectal polyp or adenoma. These mushroom-shaped growths are usually benign, but some develop into cancer over time. Localized bowel cancer is usually diagnosed through colonoscopy. The most common form of colon cancer is adenocarcinoma. Other, rarer types include lymphoma, adenosquamous and squamous cell carcinoma.

Colon cancer staging is an estimate of the amount of penetration of a particular cancer. It is performed for diagnostic and research purposes, and to determine the best method of treatment. The systems for staging colorectal cancers depend on the extent of local invasion, the degree of lymph node involvement and whether there is distant metastasis.

Definitive staging can be done after surgery has been performed and pathology reports reviewed or after a colonoscopic polypectomy of a malignant pedunculated polyp with minimal invasion. Preoperative staging of rectal cancers may be done with endoscopic ultrasound.

The most common staging system is the TNM (for tumors/nodes/metastases) system, from the American Joint Committee on Cancer (AJCC). The TNM system assigns a number based on three categories. “T” denotes the degree of invasion of the intestinal wall, “N” the degree of lymph node involvement, and “M” the degree of metastasis. The broader stage of a cancer is usually quoted as a number I, II, III, IV derived from the TNM value grouped by prognosis; a higher number indicates a more advanced cancer and likely a worse outcome.

The treatment depends on the stage of the cancer. When colorectal cancer is caught at early stages (with little spread), it can be curable. For example, invasive cancers that are confined within the wall of the colon (TNM stages I and II) are often curable with surgery. If left untreated, they spread to regional lymph nodes (stage III). Cancer that metastasizes to distant sites (stage IV) is usually not curable.

Colorectal cancer is a disease originating from the epithelial cells lining the colon or rectum of the gastrointestinal tract, most frequently as a result of mutations in the Wnt signaling pathway that artificially increase signaling activity. The mutations can be inherited or acquired. The most commonly mutated gene in all colorectal cancer is the APC gene, which produces the APC protein—a “brake” on the accumulation of β-catenin protein. Without APC, β-catenin accumulates to high levels and translocates into the nucleus, binds to DNA, and activates the transcription of genes that are normally important for stem cell renewal and differentiation but when inappropriately expressed at high levels can cause cancer. While APC is mutated in most colon cancers, some cancers have increased β-catenin because of mutations in β-catenin (CTNNB1) that block its degradation, or they have mutation(s) or other genes with function analogous to APC such as AXIN1, AXIN2, TCF7L2, or NKD1.

Beyond the defects in the Wnt-APC-beta-catenin signaling pathway, other mutations must occur for the cell to become cancerous. The p53 protein, produced by the TP53 gene, normally monitors cell division and kills cells if they have Wnt pathway defects. Eventually, a cell acquires a mutation in the TP53 gene and transforms the tissue from an adenoma into an invasive carcinoma.

Other apoptotic proteins commonly deactivated in colorectal cancers are TGF-β and DCC (Deleted in Colorectal Cancer). TGF-β has a deactivating mutation in at least half of colorectal cancers. Sometimes TGF-β is not deactivated, but a downstream protein named SMAD is. DCC commonly has deletion of its chromosome segment in colorectal cancer.

Surgery remains the primary treatment, while chemotherapy and/or radiotherapy may be recommended depending on the individual patient's staging and other medical factors. As such, it can be a challenge to determine how aggressively to treat a particular patient, especially after surgery.

Surgeries can be categorized into curative, palliative, bypass, fecal diversion, or open-and-close. Curative surgical treatment can be offered if the tumor is localized. Very early cancer that develops within a polyp can often be cured by removing the polyp (i.e., polypectomy) at the time of colonoscopy.

In colon cancer, a more advanced tumor typically requires surgical removal of the section of colon containing the tumor with sufficient margins, and radical en-bloc resection of mesentery and lymph nodes to reduce local recurrence (i.e., colectomy). If possible, the remaining parts of colon are anastomosed to create a functioning colon. In cases when anastomosis is not possible, a stoma (artificial orifice) is created. Curative surgery on rectal cancer includes total mesorectal excision (lower anterior resection) or abdominoperineal excision.

Chemotherapy is used to reduce the likelihood of metastasis developing, shrink tumor size, or slow tumor growth. Chemotherapy is often applied after surgery (adjuvant), before surgery (neoadjuvant), or as the primary therapy (palliative). The treatments listed here have been shown in clinical trials to improve survival and/or reduce mortality rate and have been approved for use by the U.S. Food and Drug Administration. In Stage I colon cancer, no chemotherapy is offered, and surgery is the definitive treatment. The role of chemotherapy in Stage II colon cancer is debatable and is usually not offered unless risk factors are identified. It is also known that the people who carry abnormalities of the mismatch repair genes do not benefit from chemotherapy. For Stage III and Stage IV colon cancer, chemotherapy is typically an integral part of treatment.

If cancer has spread to the lymph nodes or distant organs, which is the case with stage III and stage IV colon cancer respectively, adding chemotherapy agents fluorouracil, capecitabine or oxaliplatin increases life expectancy. If the lymph nodes do not contain cancer, the benefits of chemotherapy are controversial. If the cancer is widely metastatic or unresectable, treatment is then palliative. Typically in this setting, a number of different chemotherapy medications may be used. Chemotherapy drugs for this condition may include capecitabine, fluorouracil, irinotecan, oxaliplatin and UFT. The drugs capecitabine and fluorouracil are interchangeable, with capecitabine being an oral medication while fluorouracil being an intravenous medicine. Some specific regimens used for CRC are FOLFOX, FOLFOXIRI, and FOLFIRI. Antiangiogenic drugs, such as bevacizumab, ramucirumab, afilbercept, and regorafenib, are often added in first line therapy. Another class of drugs used in the second line setting are epidermal growth factor receptor inhibitors, such as cetuximab and panitumumab.

Colorectal cancer patients that have a mutation in the KRAS gene do not respond to therapies that inhibit the epidermal growth factor receptor (EGFR), such as cetuximab and panitumumab. Patients are now tested for the KRAS gene mutation before being offered these EGFR-inhibiting drugs. However, having the normal KRAS version does not guarantee these drugs will benefit the patient.

Immunotherapy with immune checkpoint inhibitors, such was nivolumab and pembrolizumab, has been found to be useful for a type of colorectal cancer with mismatch repair deficiency and microsatellite instability.

IV. Kits and Diagnostics

Kits are envisioned containing diagnostic agents, therapeutic agents, and/or other therapeutic and delivery agents. The kit may comprise reagents capable of use in determining the expression level of at least a portion of the genes listed in Table 1. For example, reagents of the kit may include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 probes that constitute a Nanostring nCounter codeset, as well as reagents to prepare the target nucleic acids for analysis.

The kit may also comprise a suitable container means, which is a container that will not react with components of the kit, such as an eppendorf tube, a syringe, a bottle, or a tube. The container may be made from sterilizable materials such as plastic or glass.

The kit may further include an instruction sheet that outlines the procedural steps of the methods, such as the same procedures as described herein or are otherwise known to those of ordinary skill.

V. Examples

The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

Example 1—Patients and Samples

CRCSC training and validation data sets: CRCSC data sets were used to train and validate the CMS classifier. Samples that were part of the “consensus” set in Guinney et al. (2015) were used for this exercise, meaning that they had “gold standard” CMS status, defined based on agreement among the six different subtyping systems, against which they could be compared to assess classification accuracy. Table 2 shows the data sets used, including tissue type (fresh frozen (FF) or formalin-fixed paraffin embedded (FFPE)), total number of samples, total number of consensus samples, and number used in data set V1 (used for training the classifier) and V2 (used in validating the classifier).

TABLE 2 Summary of data sets used in training and validating the CMS classifier. Dataset Platform Tissue Total Consensus Samples V1 (train) V2 (validate) AMC_AJC Affy HG133plus2 FF 90 80 49 31 CII French Affy HG133plus2 FF 556 466 273 185 (GSE33113) NKIAZ Affy HG133plus2 FF 62 51 33 18 (GSE35896) G5E13067 Affy HG133plus2 FF 74 57 31 25 G5E13294 Affy HG133plus2 FF 155 124 66 58 G5E14333 Affy HG133plus2 FF 157 129 72 57 G5E20916 Affy HG133plus2 FF 90 45 22 23 G5E23878 Affy HG133plus2 FF 35 24 18 6 G5E37892 Affy HG133plus2 FF 130 107 64 41 KFSYSCC Affy HG133plus2 FF 307 229 125 102 PETACC3 Affy ADXCRC FFPE 688 526 315 211 TCGA RNAseq FF 573 449 261 189 GSE2109 Affy HG133plus2 FF 293 244 0 239 G5E17536 Affy HG133plus2 FF 177 147 0 144 Total 3396 2678 1329 1329

Several subsets of the validation dataset (V2) can also be considered separately to investigate different aspects of the classifiers' performance. Besides assessing classification performance on the entire validation data set (V2), performance for these subsets was also considered:

-   -   Out of sample Validation Data (V2o): The validation data set         contained one data set (GSE2109) for which none of the samples         were in the training data set V1, so performance on this data         set consisting of 239 samples is also given to highlight the         out-of-sample predictive accuracy.     -   Affymetrix Validation Data (V2a): A subset of 929 of the V2         samples were Affymetrix data, and these are also summarized to         highlight the performance of classifiers in the Affymetrix, FF         setting.     -   Affymetrix Probeset Validation Data (V2ap): A subset of the 929         samples in the Affymetrix validation data set, 713 samples in         total, had fRMA probe-level data, making it feasible to assess         individual probe sets for strength of signal and use in         designing the Nanostring code sets. This subset was assessed to         compare single-probe and multiple-probeset Affymetrix         quantifications for the best CMS classifier.     -   RNAseq Validation Data (fCGA): The TCGA data set was the only         one comprised of RNAseq data, so predictive performance was also         summarized on the 189 TCGA samples in the validation data set V2         to assess performance of the classifier for RNAseq data.

Preprocessing: The data were preprocessed as described in the online methods in Guinney et al. (2015). Briefly, the data sets from the Gene Expression Omnibus were normalized using robust multi-array average (RMA) implemented in the affy package (Gautier et al., 2004), and all private and public Affymetrix data sets were renormalized using single-sample frozen RMA method (McCall et al., 2010) in.frma package of Bioconductor. Level 3 TCGA RNAseq data were downloaded for colon and rectal sites from the TCGA data portal (January 2014). The counts were normalized using RSEM (Li & Dewey, 2011) and then log transformed. ComBat (Johnson et al., 2007) was used to adjust for batch effects between GA and HiSeq samples.

Outliers were detected and removed from further analysis. A sample was deemed an outlier if it was flagged based on the distribution of gene expression values using the arrayQualityMetrics package (Kauffman et al., 2009) and either had a distance greater than 2.5 in the plot of the first two principle components or was flagged based on pairwise distance in the arrayQualityMetrics package.

For the various data sets, representative probe sets were selected for each gene based on consistency from calculating a “correlation of correlations” to a reference data set. ComBat was used to remove data set (batch) effects, and genes were further filtered using a variability and correlation threshold. This resulted in a set of 5,973 genes with expression values for each sample in the combined data set. Quantile normalization was subsequently used using this set of 5,973 genes.

Nanostring Technical Variability Pilot Study: A pilot study to assess the technical reproducibility of the Nanostring assay. FFPE samples from six patients were run on the custom Nanostring platform described below with 472 signature genes and 28 housekeeping genes, normalizing as described below, all in duplicate.

Nanostring Paired FF/FFPE Data Set: Matched FF and FFPE samples were obtained from 78 patients who were part of the CRCSC data set within Guinney et al. (2015), plus FFPE alone for another 7 patients, and FF alone for another 2 patients. All of these samples were part of the CRCSC “consensus samples” that have “gold standard” CMS calls, but none of them were used in the CRCSC training data set V1 used for training the classifier. Matching FF and FFPE samples were obtained from an additional 73 patients who were not part of the CRCSC cohort, and so did not have a gold standard CMS classification. The criteria for patient and sample selection are described below and reflect patients with locoregional colonic adenocarcinoma after adequate clinical staging as per the NCCN guidelines (D'Angelica et al., 2002). For sample selection and processing, the criteria applied by TCGA were implemented as well as additional criteria like tissue block with tumor not facing the block and covered by formalin to lower the likelihood of RNA degradation.

Inclusion and exclusion criteria for patient selection for UH2: Patients were included based on adequate clinical staging, operation at UTMDACC, and pathologic stage III primary colon adenocarcinoma.

Sample/specimen inclusion criteria for UH2: Samples having matched frozen and FFPE samples available were used. In addition, more than one block of tumor needed to be available from resection specimens. Archival blocks with tumor were not exposed on the paraffin block. Tumor cellularity was greater than 60% after microdissection for signet ring or mucinous well differentiated adenocarcinoma. Tumor necrosis was less than 20%.

Histopathologic Review/RNA Extraction and Pre-analytical Quality Control: Fresh frozen and selected FFPE blocks were processed at the Research Histopathology Core Facility to procure Hematoxylin & Eosin (H&E) and unstained slides. Snap frozen samples were cut with frozen section microtome from OCT block and H&E stained sections prepared from each piece of frozen tissue of the tumor. The H&E stained sections were reviewed by an experienced gastrointestinal pathologist. Reviews included tumor cellularity as well as necrosis and area of the tumor with highest cellularity were marked. The part of tumor in the OCT block corresponding to the marked tumor on H&E section was macrodissected and tissue sent to the Bio-specimen Extraction Core (BER) in dry ice. No more than 10 frozen specimens were processed each day by the RHCF. Formalin-fixed paraffin embedded tissue from the selected block was cut to prepare 5 micron thick section on 21 slides. The first and 21st slides were stained and reviewed for tumor cellularity, necrosis and area of the tumor with highest cellularity. The matching area from slides 2 to 22 were macrodissected by BER for nucleic acid extraction.

DNA and RNA was be extracted from frozen samples by AllPrep DNA/RNA minikit (QIAGEN, Netherlands) and from FFPE by AllPrep DNA/RNA FFPE kit (QIAGEN, Netherlands). These kits were selected because prior studies validating the nCounter system used the same kit. The quantity and quality of extracted RNA was measured by the Agilent 6000 Nano Assay and Agilent 6000 pico assay. The calculated RNA concentration, RNA integrity number (RIN), ribosomal ratio and electropherogram with RNA picks for each sample by both methods was stored in a database.

Gene Expression Analysis: The extracted RNA from frozen samples was divided into two and run on the Nanostring nCounter with custom codesets as described below as well as by Affyrnetrix, and the extracted RNA from FFPE samples was run on the Nanostring nCounter with custom codesets.

The design of the customized Nanostring codesets is described below. The nCounter conditions were optimized for these customized codesets after discussion with Nanostring. Both aliquots from the frozen samples and the paraffin samples were analyzed by the nCounter system. A maximum of 2 (12 samples per cartridge) cartridges were run in one experiment to minimize the chances of faded signals.

Preprocessing of Nanostring Data: The Nanostring QA/QC guidelines were followed to decide on the need of repeating the array for a sample. The nCounter gene expressions were first normalized using Nanostring's internal positive and negative controls, as recommended, and then dividing by the geometric mean of the 28 reference genes. Since the set of 472 signature genes were selected to have moderate or high expression values, no issues of low expressing genes were encountered, as documented by some researchers working with the Nanostring nCounter system. As described below, for each sample, quantile normalization was performed to map the Nanostring/FFPE expression values onto the scale of the samples in the CRCSC training data in order to enable direct application of the classifier trained on the large CRCSC data set.

Assessing performance of CMS classifier assay in a CLIA certified laboratory: The Nanostring assay with top 200 genes (CRC CMS-200) including those in CMS-100, was further validated at a CLIA-certified Molecular Diagnostic Laboratory (MDL) to apply this assay as one of integral biomarker for a phase II clinical trial assessing safety and efficacy of dual TGF-β trap: anti-PD-L1 molecule M7824 (EMD-Serono) in CMS4 subtype of metastatic colon cancer. Validation was performed before the assay was used as an integral biomarker. Thirty-five tumor samples were used to validate the assay across 10 runs for total of 120 reactions. All 35 samples were included in the CRCSC study and gold standard CMS was known for these samples. Input for the assay was 250 ng of total RNA extracted from FFPE tumor tissue with 20% or higher tumor cellularity. The design of the codesets was similar to those built for the research laboratory, except the oligonucleotides for codesets were provided to the Nanostring by the Integrated DNA technologies. Accuracy, analytical sensitivity, and analytical specificity were assessed by comparing calls MDL CMS panel with CRCSC Affymetrix reported calls (gold standard as described earlier). Reproducibility was assessed across original run and 3 or more repeat runs without re-extraction of RNA, repeat runs with re-extraction of RNA and by 2 technicians. A positive (CMS 4) control of previously tested CMS4 sample and negative control of previously tested non-CMS4 were run alongside each run. A tumor is classified in to a CMS if the gene expression score (probability of a CMS) for the subtype was >0.58. A tumor with gene expression score>0.45 or <0.58 for any CMS was classified to have mixed CMS.

Assessing performance of CMS classifier as a prognostic marker in stage IV colorectal cancer: Patients with a CMS determination from the Nanostring based gene expression score were pooled from three separate sources, including two clinical trials; a phase II trial assessing safety and efficacy of dual TGF-β trap: anti-PD-L1 molecule M7824 (EMD-Serono) in CMS4 subtype of colon cancer (n=125) and a phase II trial assessing trametinib and durvalumab in microsatellite stable colorectal cancer (n=19) and Assessment of Targeted Therapies Against Colorectal Cancer (ATTACC Program) Screening Protocol (n=103). ATTACC is a clinical protocol which enrolled patients with stage IV/treatment refractory colorectal cancer for molecular characterization of tumor tissue. The ATTACC samples and the samples from the trametinib/durvalumab study were characterized by CRC CMS-100 assay at the research molecular diagnostic laboratory, while samples from patients enrolled in M7824 clinical trial were characterized by CRC CMS-200 performed at the molecular diagnostic laboratory. Since all three studies were performed in patients with stage IV CRC and high (>90%) concordance between CRC CMS-100 vs. CRC CMS-200, we combined the three data sources to improve our power to detect subtype specific survival differences. Median overall survival was calculated from date of stage IV diagnosis to death or date of last follow up, which was censored. Survival patterns were visualized with Kaplan Meier survival curves and compared using the log-rank test. Graphs were generated using IBM SPSS Statistics 24.

Example 2—Statistics Methods

Development and Validation of CMS Classifier on CRCSC: An analytical workflow was used to build an optimized classifier mainly designed for FF samples using the CRCSC data, upon which the FFPE-based classifier was also based. The goals were to (1) develop an optimized classifier and (2) find out how parsimonious the classifier could be while still yielding acceptable classification accuracy. A more parsimonious classifier with fewer genes has greater potential for validation for clinical use.

Following are the steps that were followed in building and validating this classifier:

Step 1. As described above, the CRCSC data sets were split into a training data set, V1, consisting of 1,329 samples from 12 studies, and a validation data set, V2, including 1,329 samples from 14 studies. The performance of a subset of the validation data that were Affymetrix U133Plus2.0 arrays was also considered, which consisted of 929 Samples from 12 Studies (V2a), a subset of those that had fRMA probe-level data consisting of 713 samples from 10 studies (V2ap), the subset of TCGA validation data to summarize performance for RNAseq data (TCGA), and also summarized results from GSE2109 separately (V2o) to provide a summary of out-of-sample performance since no samples from this study were used in the training data VI.

Step 2. Four-fold cross validation was used on the V1 data set to build classifiers using a number of different modeling strategies and reduced gene lists. Modeling strategies and gene lists are given below. Here, the four-fold cross validation procedure is described in more detail.

Step 2a. The training data set V1 was split into four subsets, containing 332, 332, 332, and 333 samples, respectively, for use in a four-fold cross validation model building strategy.

Step 2b. For each modeling strategy, the model was fit to each of the four 3/4 subsets, optimizing tuning parameters using nested cross validation, and assessed accuracy for predicting the gold standard CMS on the left-out 1/4 subset. Tuning parameters that showed the best accuracy were selected as optimal parameters for each subset.

Step 2c. The predictive accuracy of each modeling strategy was summarized as a function of number of genes, allowing us to both assess which modeling strategy appears to be best and what minimum number of genes are needed for accurate CMS classification.

Step 3. Choosing the best modeling strategy, the classification accuracy was computed in the validation data set V2 as well as the various subsets mentioned above, and the results were again summarized as a function of number of genes in the model.

Classification Modeling Strategies: The following classification modeling strategies were considered:

Linear Discriminant Analysis: A principal component decomposition of the training data sample-by-gene matrix was performed, and then a linear discriminator was built. The sole tuning parameter was the number of principal components kept.

Quadratic Discriminant Analysis: After principal component decomposition, a quadratic discriminator was built and, once again, the only tuning parameter was the number of principal components to keep.

K-Nearest Neighbor: Each test sample was assigned to the most common class among its k-nearest neighbors in training data. Various distance metrics based on kernel densities can be used to find k-nearest neighbors. The Rpackage knn developed by Schleip et al. (2016) was used to fit k-nearest neighbor models. The tuning parameters were k (the number of neighbors) and the kernel used in the distance matric. k from 1 up to 15 was considered, and triangular, rectangular, Epanechnikov, and optimal kernels were considered. See Schleip et al. (2016) for details of the kernels.

Random Forest: The R package random.Forest developed by Breiman (2001) was used to fit random forest models. A random forest classifier aggregates across a number of classification trees fitted to a series of random subsets of the given data set. The procedure proceeds as follows:

-   -   1. Draw N bootstrap samples from the original data set.     -   2. For each bootstrap sample, grow a regression tree. At each         node, randomly sample m of the predictors and choose the best         split among those variables.     -   3. Predict the new data by aggregating the predictions across         the N trees.

Tuning parameter was predictor size m with N fixed at 500. Ten values of m that were equally spaced from 1 up to the maximum number of predictors were considered.

Rotation Forest: Rotation forests (Rodriguez & Kuncheva, 2006) combine together principal component decompositions and random forests. The steps are:

-   -   1. Draw N bootstrap samples from the original data set.     -   2. For each bootstrap sample, grow a regression tree. Split the         predictors into K subsets. For each subset, rotate the         predictors by using a principal component decomposition         performed on the given bootstrap data and subset of predictors.     -   3. Predict the new data by aggregating the predictions of the N         trees.

Tuning parameter was K with N fixed at 500. Ten values of K that were equally spaced from 1 up to the half of the maximum number of predictors were considered.

Weighted Support Vector Machine (wSVM): The function svm was used in the R package e1071 to fit the support vector machines. The settings Type=“C-classification” and scale=F were chosen, and radial, linear, and polynomial kernels and cost functions 0.1, 0.5, 1, 5, 10, 50, 100, 150, and 200 were considered. Pairwise coupling was used to produce probability estimates for each CMS (see Ting-Fan Wu et al., (2004)). In order to take imbalance among CMS in the training set into account, class weights were given as 1/the number of training samples belonging to each class. These weights allowed larger penalty for misclassification in smaller class when optimizing the SVM objective function, with the hope of obtaining a classifier with more even performance across CMS. See Chih-Chung Chang & Chih-Jen Lin (2001) for details on how class weights work.

Distance-weighted Discrimination (DWD): Distance-weighted Discrimination (DWD; Marron et al., 2007) finds a separating hyperplane minimizing the sum of reciprocals of distances from observations to the hyperplane. DWD was originally proposed to overcome the “data filing” issue when n<p in the support vector machine. This method was extended to the multiple-class setting by Huang et al. (2013). The proposed optimization problem in DWD can be reformulated as loss+penalty. The penalty term involves in a parameter that controls the mount of the penalty. This penalty parameter was the sole tuning parameter. Eight different values of the penalty parameters were considered: 100, 200, 400, 800, 1200, 1600, 2000, and 2400.

Ensemble Methods: Ensemble methods were also considered in which the consensus was taken after applying multiple classifiers described above. If one class had the most votes, select it and stop. Otherwise, drop the classifier with the lowest 4-fold CV performance and repeat. Ensembles including the best 3, 4, and 5 classification methods, as determined by 4-fold CV, were considered.

Gene Ranking Strategy: In order to compare classification strategies and evaluate the minimum number of genes for accurate classification, the genes were ordered in decreasing order of their classification importance and each classification strategy was applied for a range of model sizes, ranging from 5,973 genes all the way down to 2 genes. A boosting procedure was designed based on multi-class AdaBoost (Zhu et al., 2009) to order the genes. The basic idea is that samples are repeatedly re-weighted during the algorithm so that the next best gene can focus more on samples that were misclassified at the previous step. The procedure can be viewed as a forward stage-wise additive selection. The boosting procedure proceeds as follows:

-   -   1. Given N samples in the data set, initialize the sampling         weights w₁=1 for i=1, . . . , N.     -   2. Initialize the outcome gene list A={ }.     -   3. Initialize the candidate gene list C={1, . . . , G}, where         G=5973 is the initial set of genes.     -   4. From m=1, . . . , G         -   a. Fit a weighted multinomial logistic regression for each             gene in C.         -   b. For each gene, compute the weighted misclassification             rate

${Err}_{g} = \frac{\sum\limits_{i = 1}^{N}\;{w_{i}{I\left( {{sample}\mspace{14mu} i\mspace{14mu}{misclassified}\mspace{14mu}{in}\mspace{14mu}{model}\mspace{14mu}{for}\mspace{14mu}{gene}\mspace{14mu} g} \right)}}}{\sum\limits_{i = 1}^{N}\; w_{i}}$

-   -   -   c. Add the best gene with smallest Err=min(Err_(g)) to set             A, and remove it from set C.         -   d. Compute α=log{(1−Err)/Err}+log(K−1), where K=4 is the             number of classes.         -   e. Update the weights to weight misclassified samples more             heavily             -   w_(i)=w_(i){α×I(sample i is misclassified)} with I{•} an                 indicator function.

This procedure produced a list of genes ranked in descending order of their CMS classification importance. By using the same reduced gene sets for each classification method, a straightforward comparison of which method appears to perform better was gained, to find the minimum gene set size yielding good classification performance, to fairly compare the various methods at any desired model size, and to consider ensemble methods that involve combinations of individual methods.

Details of wSVM classifier: The results below reveal that the best classifier was the wSVM. Here, how to apply this classifier is described in detail. The user calls the wSVM function with an N by P matrix of expression values for P genes for each of N samples with the column names as Entrez IDs, and the function will quantile normalize the data and apply the wSVM to get class predictions. After pairwise coupling, probabilities of each CMS are obtained for sample i, π_(ij) such that Σ_(j=1) ⁴π_(ij)=1, with α_(i)=max_(j){π_(ij)} indicating the highest CMS class probability for that sample, which is considered a measure of CMS classification confidence. There are two possible rules to classify a sample into a CMS group based on these measures:

-   -   1. Most Likely CMS: Classify sample i into the most likely CMS,         {j: π_(ij)=max(π_(ij))}, regardless of classification confidence         α_(i). One benefit of this strategy is that it provides a         predicted CMS for each sample.     -   2. Confidence Threshold: Classify sample i into the most likely         CMS as long as the classification confidence α_(i) is above some         threshold δ (e.g. 0.50, 0.80 or 0.90), and otherwise consider         indeterminate {Choose CMS j: π_(ij)=max(π_(ij)) if π_(ij)>δ,         otherwise CMS indeterminate}. This strategy most closely matches         the classification strategy used in the classifiers presented in         Guinney et al. (2015).

Design of Custom Nanostring Codesets: Custom Nanostring Code sets were designed using the genes present in the best classifier from the CRCSC data set. As can be seen in the results, the classifier obtained excellent performance even for signatures with as few as 50-100 genes, which is an order of magnitude that would be feasible for a clinical assay. However, for initial Nanostring design, approximately 500 genes were selected, with the expectation that only a subset of these genes would show high FF/FFPE concordance for the reasons mentioned above, leaving us with enough genes with high concordance to use in our Nanostring FFPE classifier.

A custom Nanostring platform was designed with 500 code sets including 472 signature probe sets and 28 reference probe sets. First, the top 500 genes from the boosting procedure were chosen. The code sets were designed for each gene to be located in the genomic region containing the single Affymetrix 133-2 Plus2.0 probe set that was most highly correlated with the fRMA gene-level expression level for each of the top 500 genes, and the best 472 for which the correlation between the probe set and gene-level summaries were at least 0.70 (54% had correlations of at least 0.90) were selected. This is a novel customized approach for choice of probe sets compared with NanoString's usual strategy of selecting code sets for genes themselves, and this approach is more likely to lead to a FFPE classifier recapitulating the accuracy of our Affymetrix-based CRCSC classifier.

The wSVM classifier was retrained using only these specific 472 genes with the CRCSC training data set V1 and applying this classifier to 713 samples in the CRCSC validation data V2 that had fRMA probe-level measurements (V2ap) using only these single-probe measurements mapped to the scale of the gene-level summaries by affine transformation. These results give an upper bound on the performance that can be expected for the Nanostring, FFPE-based classifier built based on code sets designed to match these genes.

Reference Code Sets: The 28 reference code sets were selected from an initial set of 74 candidates were assembled that included the reference probe sets on the Nanostring PanCan array plus the most commonly used reference genes from a thorough survey of all published papers involving Nanostring nCounter expression data in cancer from 2015-2016, selecting those with small variance and evidence of no difference CMS groups (p>0.05 inANOVA) in the preliminary data (Chen et al., 2015; Ligibel et al., 2015; Prat et al., 2016; Veldman-Jones et al., 2015; Wallden et al., 2015).

Nanostring Pilot Study Analysis: For the six samples run in duplicate on the custom Nanostring code set, linear mixed models were fit for each of the 472 signature genes with a random effect per sample, the biological variability σ² _(u), and technical variability σ² _(e) were estimated, and then the percent variability explained by biological factors, PBV=σ² _(u)/(σ² _(u)+σ² _(e))×100, was computed with values near 100% indicating negligibly low levels of technical variability and smaller values indicating substantial technical variability. The coefficient of variation was also calculated for each gene, computed as CV=σ_(e)/μ. Then, the distribution of PBV and CV across genes was summarized by a six-number summary, including minimum, maximum, 25th percentile (Q25), 75th percentile (Q75), median (QSO), and mean.

Development of Nanostring Classifier: Rather than trying to train the CMS classifier anew on our 178 FFPE samples, instead the following novel strategy was used to port the CMS classifier designed for Affymetrix platform on FF samples over to the Nanostring/FFPE setting that efficiently utilizes the vast information available in the CRCSC training and validation data sets.

-   -   1. Identify a reduced gene set with high concordance between         tissue and platform type. Individual genes with r>0.80 across         paired measurements were chosen, computed using patients for         which their patient-specific FF/FFPE correlation was greater         than 0.75. Note that this custom Nanostring platform was         purposefully built to have more codesets (472) than needed for         accurate CMS classification so that even after filtering out         genes with low Nanostring/Affymetrix and/or FF/FFPE concordance,         enough (˜100) would be left to have accurate CMS classification         for the Nanostring/FFPE setting.     -   2. Train a new model with the reduced gene set on the CRCSC         training data V1, assess accuracy for Affymetrix/FF samples         using CRCSC validation data V2 to obtain new wSVM classifier rp.     -   3. Quantile normalize the Nanostring expression values X_(i.g)         ^(N) ^(FFPE) for the chosen gene set for each FFPE sample to the         scale of the Affymetrix/FF samples in the CRCSC data set, e.g.         X_(i.g) ^(*A) ^(FF) =f_(1,g) (X_(i.g) ^(N) ^(FFPE) ).     -   4. Apply the wSVM classifier yr from step 2 to the quantile         normalized expressions X_(i.g) ^(*A) ^(FF) to obtain a CMS         classification and compute the probability for each CMS type         π_(ij), with α_(i)=max(π_(ij)) being the probability of the CMS         with the highest probability.

Classify the subject into a CMS using one of the two rules described above, assigning to the “most likely CMS” with the highest probability or imposing a “confidence threshold” (e.g. α_(i)>0.50, 0.80, or 0.90), defining the sample as “indeterminate” if below the confidence threshold. The classification accuracy of this strategy was assessed on 85 FFPE samples by comparing classification results with the gold standard “consensus” CMS calls from Guinney et al. (2015) for these samples, and to the 73 FFPE samples that were not part of the CRCSC cohort using the Affymetrix FF 4 72 gene classifier results as a pseudo-gold standard.

Sample Quality Assessment: For each FF and FFPE sample, RIN was computed to summarize the RNA quality, and for FFPE samples %200 nt was also computed as an additional QC statistic. These quantities were summarized through box plots and distributional summaries, and correlated them with sample-specific FF/FFPE correlation to assess whether samples with poor FF/FFPE correlation tended to have poor RNA quality, and correlated with CMS classification accuracy to assess how classification accuracy varied across RNA quality.

Example 3—Nanostring FFPE Classifier

Comparison of Methods on CRCSC Data Set, 4-fold Cross Validation of Training Data V1: FIG. 2 and Table 3 present the classification accuracy of each proposed method using 4-fold cross validation of the training data set (V1) for models of various size from 5 genes up to the full set of 5,973 genes considered in Guinney et al. (2015), with gene lists determined by Multi-class Adaboost as described above. For all methods, classification was forced into a single CMS (“most likely CMS”) and compared with the gold standard “consensus” CMS from Guinney et al. (2015).

All methods performed well, with performance that decayed only slowly with the number of genes. Improved performance was seen with refined gene lists smaller than the original 5,973 genes, with outstanding performance even for smaller models with several methods having 4-class accuracy of >0.94 for the 75 gene model, and >0.90 even for some of the 20-50 gene models.

The wSVM, SVM, and DWD methods appeared to have the best performance both for the full 5,973-gene model as well as the models with smaller gene sizes. Looking at performance by CMS (Tables 4A-7E), CMS3 was consistently the hardest subtype to classify across methods and gene sizes. For example, for the 500-gene model, the classification accuracies for CMS1 (0.959, 0.950, and 0.973 for wSVM, SVM, DWD, respectively), CMS2 (0.974, 0.976, and 0.972, respectively), and CMS4 (0.966, 0.952, and 0.946, respectively) were higher than CMS3 (0.926, 0.920, and 0.938, respectively).

Among these three methods, wSVM was chosen as the classifier moving forward. It was chosen over the SVM because of its more even performance across CMS, with better performance for the difficult CMS3, and over DWD because its ease of application relative to the DWD that requires special software to classify future samples.

TABLE 3 Classification accuracy on training data set V1 using 4-fold cross validation. V1 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.783 0.827 0.920 0.929 0.934 0.933 0.938 0.950 0.950 0.953 0.959 0.962 0.963 0.967 0.959 SVM 0.792 0.847 0.921 0.931 0.93 0.935 0.941 0.950 0.947 0.956 0.955 0.959 0.958 0.965 0.953 DWD 0.775 0.824 0.916 0.925 0.926 0.933 0.941 0.953 0.948 0.959 0.956 0.965 0.961 0.964 0.956 glmnet 0.784 0.844 0.922 0.926 0.931 0.933 0.941 0.944 0.941 0.944 0.941 0.949 0.949 0.94  0.939 PC-LDA 0.781 0.834 0.901 0.913 0.921 0.929 0.938 0.938 0.938 0.944 0.942 0.943 0.942 0.944 0.918 PC-QDA 0.791 0.833 0.908 0.919 0.924 0.933 0.938 0.946 0.948 0.957 0.959 0.953 0.954 0.949 0.916 RandomForest 0.771 0.837 0.907 0.913 0.918 0.928 0.935 0.935 0.935 0.942 0.932 0.932 0.938 0.932 0.928 RotForest 0.777 0.841 0.908 0.918 0.917 0.929 0.928 0.942 0.94  0.946 0.953 0.958 0.959 0.962 0.941 KNN 0.78  0.824 0.902 0.907 0.904 0.918 0.931 0.934 0.935 0.935 0.935 0.933 0.935 0.931 0.869 Ensemble3 0.789 0.831 0.91  0.917 0.926 0.935 0.941 0.947 0.944 0.953 0.95  0.945 0.95  0.95  0.914 Ensemble4 0.788 0.831 0.912 0.916 0.926 0.937 0.941 0.949 0.946 0.953 0.949 0.946 0.95  0.951 0.922 Ensemble5 0.789 0.837 0.916 0.926 0.932 0.938 0.947 0.95  0.95  0.953 0.953 0.953 0.958 0.959 0.938

TABLE 4A Classification accuracy of CMS1 samples on training data set V1 using 4-fold cross validation as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.718 0.777 0.927 0.918 0.941 0.945 0.95  0.945 0.95  0.941 0.945 0.968 0.959 0.973 0.973 SVM 0.691 0.777 0.905 0.918 0.927 0.932 0.95  0.945 0.941 0.945 0.936 0.955 0.95 0.973 0.968 DWD 0.709 0.795 0.905 0.923 0.909 0.941 0.955 0.95  0.95  0.955 0.95 0.968 0.973 0.973 0.968 glmnet 0.677 0.795 0.891 0.909 0.905 0.927 0.936 0.927 0.936 0.927 0.927 0.936 0.936 0.932 0.918 PC-LDA 0.668 0.782 0.877 0.905 0.909 0.941 0.936 0.932 0.973 0.964 0.95  0.955 0.959 0.964 0.918 PC-QDA 0.686 0.782 0.905 0.941 0.918 0.955 0.959 0.955 0.982 0.982 0.968 0.968 0.977 0.973 0.9  RandomForest 0.668 0.764 0.859 0.891 0.882 0.905 0.909 0.886 0.918 0.927 0.909 0.905 0.918 0.909 0.895 RotForest 0.668 0.764 0.868 0.891 0.905 0.914 0.905 0.914 0.909 0.909 0.941 0.945 0.945 0.95  0.927 KNN 0.695 0.773 0.859 0.877 0.855 0.882 0.891 0.905 0.909 0.918 0.923 0.918 0.914 0.9  0.768 Ensemble3 0.695 0.786 0.882 0.886 0.909 0.932 0.923 0.945 0.959 0.95  0.95 0.955 0.968 0.936 0.882 Ensemble4 0.691 0.786 0.882 0.886 0.914 0.941 0.927 0.95  0.964 0.95  0.945 0.959 0.968 0.941 0.914 Ensemble5 0.695 0.777 0.891 0.909 0.918 0.941 0.955 0.95  0.95  0.959 0.95  0.964 0.973 0.968 0.932

TABLE 4B Classification accuracy of CMS1 samples on validation data set as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.716 0.793 0.875 0.862 0.866 0.901 0.905 0.931 0.914 0.927 0.957 0.94 0.94 0.944 0.931 SVM 0.664 0.793 0.866 0.871 0.875 0.901 0.905 0.931 0.914 0.94 0.948 0.948 0.944 0.935 0.94 DWD 0.69 0.776 0.858 0.862 0.879 0.897 0.901 0.918 0.905 0.927 0.94 0.953 0.944 0.944 0.953 glmnet 0.659 0.784 0.841 0.858 0.853 0.888 0.905 0.914 0.879 0.909 0.931 0.905 0.901 0.927 0.922 PC-LDA 0.651 0.793 0.866 0.853 0.862 0.866 0.871 0.892 0.905 0.914 0.914 0.944 0.944 0.931 0.922 PC-QDA 0.672 0.776 0.875 0.875 0.866 0.901 0.905 0.892 0.892 0.909 0.914 0.935 0.927 0.909 0.884 RandomForest 0.677 0.759 0.858 0.862 0.879 0.866 0.892 0.897 0.892 0.909 0.901 0.909 0.909 0.897 0.918 RotForest 0.655 0.767 0.858 0.866 0.892 0.888 0.905 0.905 0.914 0.922 0.944 0.944 0.914 0.927 0.914 KNN 0.694 0.793 0.841 0.841 0.828 0.853 0.866 0.875 0.858 0.849 0.866 0.853 0.849 0.832 0.711 Ensemble3 0.703 0.78 0.862 0.849 0.871 0.862 0.897 0.892 0.897 0.918 0.922 0.927 0.918 0.909 0.866 Ensemble4 0.703 0.776 0.866 0.853 0.879 0.862 0.897 0.892 0.897 0.922 0.922 0.931 0.918 0.914 0.892 Ensemble5 0.69 0.78 0.866 0.871 0.875 0.875 0.897 0.901 0.897 0.927 0.935 0.944 0.935 0.931 0.927

TABLE 4C Classification accuracy CMS1 samples with out of sample prediction set as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.694 0.776 0.837 0.837 0.878 0.918 0.878 0.959 0.939 0.939 0.98 0.98 0.98 0.959 0.918 SVM 0.612 0.816 0.796 0.857 0.878 0.918 0.898 0.98 0.918 0.98 1 1 0.98 0.959 0.959 DWD 0.612 0.776 0.816 0.857 0.898 0.898 0.918 0.939 0.939 0.98 1 0.98 0.98 0.98 0.98 glmnet 0.592 0.776 0.796 0.857 0.857 0.898 0.939 0.918 0.857 0.918 0.98 0.939 0.939 0.98 0.959 PC-LDA 0.592 0.796 0.857 0.918 0.939 0.898 0.857 0.878 0.939 0.939 0.939 0.959 0.98 0.939 0.939 PC-QDA 0.612 0.776 0.857 0.898 0.878 0.878 0.898 0.898 0.918 0.939 0.959 0.98 0.959 0.959 0.939 RandomForest 0.673 0.735 0.878 0.898 0.898 0.878 0.939 0.959 0.959 0.959 0.98 0.98 0.98 0.98 0.98 RotForest 0.653 0.755 0.878 0.898 0.959 0.898 0.939 0.959 0.918 0.959 0.98 0.98 0.959 0.939 0.898 KNN 0.694 0.816 0.857 0.816 0.857 0.857 0.857 0.857 0.878 0.816 0.878 0.857 0.857 0.837 0.673 Ensemble3 0.673 0.776 0.837 0.857 0.918 0.878 0.959 0.918 0.939 0.918 1 0.98 0.98 0.98 0.918 Ensemble4 0.673 0.776 0.857 0.857 0.939 0.878 0.959 0.918 0.939 0.918 1 0.98 0.98 0.98 0.939 Ensemble5 0.653 0.776 0.857 0.878 0.918 0.878 0.939 0.918 0.918 0.918 1 0.98 0.98 0.98 0.98

TABLE 4D Classification accuracy CMS1 samples with TCGA dataset as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.897 0.897 1 1 1 1 1 1 1 0.966 0.966 0.966 0.966 1    1    SVM 0.897 0.897 1 1 1 1 1 1 1 0.966 1    1    1    1    1    DWD 0.931 0.897 1 1 1 1 1 1 1 0.966 0.931 1    0.966 1    1    glmnet 0.862 0.897 1 1 1 1 1 0.966 0.966 1 0.966 0.931 0.931 0.931 0.966 PC-LDA 0.862 0.966 1 1 1 1 1 1 1 1 1    1    1    1    1    PC-QDA 0.897 0.897 1 1 1 1 1 1 1 1 1    1    1    0.966 0.966 RandomForest 0.862 0.897 0.966 0.966 0.966 1 1 0.966 0.966 0.966 0.931 0.931 0.931 0.897 0.897 RotForest 0.897 0.897 0.966 1 1 1 1 1 1 0.966 0.966 1    1    1    0.966 KNN 0.793 0.931 0.966 0.966 0.931 0.897 0.966 0.966 0.931 0.931 0.931 0.931 0.966 0.931 0.862 Ensemble3 0.862 0.897 1 1 1 1 1 1 1 1 0.931 0.966 0.966 0.931 0.966 Ensemble4 0.862 0.897 1 1 1 1 1 1 1 1 0.931 0.966 0.966 0.931 0.966 Ensemble5 0.862 0.897 1 1 1 1 1 1 1 1 1    1    0.966 0.966 0.966 Ensemble5 0.642 0.801 0.852 0.881 0.881 0.909 0.915 0.943 0.92 0.949 0.955 0.955 0.949 0.932 0.955

TABLE 4E Classification accuracy CMS1 samples with Affymetrix set as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.642 0.801 0.852 0.881 0.881 0.909 0.915 0.943 0.92  0.949 0.955 0.955 0.949 0.932 0.955 SVM 0.67  0.773 0.847 0.869 0.886 0.898 0.903 0.92  0.909 0.943 0.955 0.955 0.949 0.943 0.96  DWD 0.659 0.784 0.824 0.858 0.852 0.892 0.903 0.926 0.886 0.915 0.955 0.926 0.926 0.96  0.943 glmnet 0.636 0.79  0.852 0.858 0.858 0.869 0.847 0.892 0.898 0.903 0.903 0.943 0.943 0.932 0.926 PC-LDA 0.653 0.773 0.869 0.869 0.869 0.903 0.903 0.886 0.881 0.903 0.909 0.938 0.926 0.909 0.903 PC-QDA 0.659 0.75  0.858 0.869 0.886 0.858 0.892 0.909 0.892 0.903 0.909 0.909 0.909 0.898 0.926 RandomForest 0.636 0.767 0.858 0.875 0.898 0.886 0.915 0.909 0.909 0.926 0.949 0.938 0.898 0.92  0.909 RotForest 0.699 0.812 0.847 0.847 0.841 0.864 0.875 0.892 0.864 0.858 0.869 0.852 0.841 0.841 0.75  KNN 0.699 0.778 0.852 0.858 0.875 0.858 0.909 0.903 0.892 0.92  0.943 0.926 0.915 0.92  0.886 Ensemble3 0.699 0.778 0.858 0.864 0.886 0.858 0.909 0.903 0.892 0.926 0.943 0.932 0.915 0.926 0.898 Ensemble4 0.676 0.784 0.852 0.875 0.875 0.875 0.903 0.909 0.898 0.926 0.938 0.943 0.938 0.938 0.932 Ensemble5 0.642 0.801 0.852 0.881 0.881 0.909 0.915 0.943 0.92  0.949 0.955 0.955 0.949 0.932 0.955

TABLE 5A Classification accuracy of CMS2 samples on training data set V1 using 4-fold cross validation as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.807 0.855 0.921 0.923 0.929 0.933 0.941 0.962 0.96  0.96  0.969 0.974 0.974 0.985 0.807 SVM 0.897 0.935 0.945 0.959 0.95  0.954 0.962 0.972 0.971 0.978 0.981 0.983 0.976 0.985 0.897 DWD 0.804 0.857 0.919 0.924 0.923 0.929 0.938 0.966 0.957 0.966 0.966 0.974 0.972 0.979 0.804 glmnet 0.904 0.928 0.948 0.952 0.95  0.955 0.959 0.974 0.971 0.967 0.962 0.969 0.971 0.962 0.904 PC-LDA 0.902 0.928 0.972 0.966 0.967 0.978 0.986 0.988 0.99  0.99  0.993 0.993 0.995 0.991 0.902 PC-QDA 0.898 0.919 0.952 0.95  0.957 0.964 0.974 0.976 0.971 0.983 0.985 0.985 0.981 0.978 0.898 RandomForest 0.871 0.938 0.952 0.957 0.959 0.974 0.972 0.974 0.971 0.974 0.969 0.964 0.972 0.971 0.871 RotForest 0.885 0.95  0.96  0.962 0.964 0.962 0.971 0.981 0.981 0.978 0.993 0.985 0.988 0.991 0.885 KNN 0.902 0.931 0.978 0.969 0.974 0.979 0.988 0.99  0.979 0.981 0.99  0.993 0.99  0.988 0.902 Ensemble3 0.874 0.873 0.974 0.967 0.969 0.981 0.981 0.991 0.986 0.986 0.986 0.991 0.991 0.986 0.874 Ensemble4 0.874 0.873 0.974 0.967 0.969 0.981 0.981 0.991 0.986 0.986 0.986 0.991 0.991 0.986 0.874 Ensemble5 0.867 0.909 0.971 0.969 0.969 0.972 0.988 0.986 0.985 0.985 0.99  0.991 0.991 0.993 0.867

TABLE 5B Classification accuracy of CMS2 samples on validation data set as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.87 0.873 0.943 0.951 0.958 0.953 0.955 0.957 0.957 0.962 0.967 0.964 0.972 0.972 0.967 SVM 0.842 0.886 0.934 0.951 0.946 0.964 0.96  0.971 0.957 0.974 0.971 0.976 0.971 0.974 0.972 DWD 0.749 0.792 0.903 0.91  0.908 0.927 0.932 0.939 0.938 0.943 0.946 0.945 0.958 0.962 0.96  glmnet 0.87 0.877 0.932 0.941 0.943 0.95  0.957 0.96  0.962 0.964 0.953 0.964 0.969 0.967 0.977 PC-LDA 0.877 0.886 0.95  0.964 0.953 0.969 0.965 0.967 0.981 0.979 0.981 0.981 0.983 0.984 0.977 PC-QDA 0.891 0.88 0.927 0.936 0.958 0.96  0.962 0.969 0.965 0.967 0.967 0.967 0.972 0.972 0.951 RandomForest 0.846 0.903 0.948 0.955 0.96  0.974 0.967 0.971 0.958 0.964 0.967 0.967 0.969 0.967 0.964 RotForest 0.889 0.912 0.943 0.955 0.965 0.96  0.967 0.972 0.969 0.967 0.977 0.969 0.967 0.976 0.969 KNN 0.87 0.906 0.96 0.962 0.974 0.981 0.981 0.972 0.974 0.977 0.984 0.988 0.988 0.983 0.976 Ensemble3 0.828 0.868 0.951 0.969 0.969 0.977 0.972 0.974 0.976 0.977 0.977 0.981 0.984 0.981 0.974 Ensemble4 0.83 0.87 0.951 0.971 0.969 0.977 0.971 0.974 0.976 0.977 0.977 0.981 0.984 0.981 0.974 Ensemble5 0.854 0.873 0.951 0.967 0.967 0.971 0.976 0.976 0.972 0.977 0.981 0.981 0.983 0.981 0.981

TABLE 5C Classification accuracy CMS2 samples with out of sample prediction set as function of number of genes. V2o 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.899 0.876 0.944 0.933 0.955 0.933 0.944 0.933 0.944 0.944 0.966 0.955 0.966 0.966 0.966 SVM 0.888 0.865 0.933 0.955 0.955 0.955 0.944 0.955 0.933 0.966 0.955 0.966 0.966 0.966 0.966 DWD 0.764 0.809 0.899 0.91  0.899 0.91  0.91 0.91  0.888 0.91  0.933 0.944 0.966 0.966 0.966 glmnet 0.91  0.888 0.921 0.91  0.933 0.921 0.921 0.955 0.944 0.955 0.933 0.933 0.944 0.989 0.989 PC-LDA 0.91  0.888 0.966 0.978 0.966 0.989 0.944 0.989 0.978 0.978 0.978 0.989 0.989 0.989 0.978 PC-QDA 0.933 0.888 0.944 0.944 0.989 0.978 0.966 0.989 0.966 0.978 0.978 0.989 0.989 0.989 0.966 RandomForest 0.865 0.899 0.966 0.966 0.966 0.989 0.978 0.978 0.978 0.978 0.978 0.989 0.978 0.978 0.978 RotForest 0.899 0.921 0.966 0.966 0.966 0.966 0.978 0.989 0.966 0.966 0.978 0.966 0.966 0.989 0.989 KNN 0.865 0.899 0.966 0.966 0.989 0.989 1 0.955 0.978 0.989 0.978 1 0.989 1 0.966 Ensemble3 0.831 0.854 0.978 0.978 0.966 0.989 0.978 0.966 0.989 0.978 0.978 0.989 0.989 0.989 0.978 Ensemble4 0.843 0.854 0.978 0.978 0.966 0.989 0.978 0.966 0.989 0.978 0.978 0.989 0.989 0.989 0.978 Ensemble5 0.899 0.865 0.978 0.978 0.978 0.978 0.978 0.978 0.978 0.978 0.978 0.989 0.989 0.989 0.989

TABLE 5D Classification accuracy CMS2 samples with TCGA dataset as function of number of genes. TCGA 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.92  0.886 0.966 0.977 0.989 0.989 0.966 0.977 0.966 0.977 0.966 0.966 0.977 0.977 0.966 SVM 0.875 0.932 0.955 0.966 0.966 0.977 0.955 0.977 0.966 0.977 0.977 0.977 0.966 0.977 0.977 DWD 0.773 0.773 0.932 0.943 0.966 0.977 0.966 0.966 0.966 0.966 0.966 0.966 0.977 0.977 0.977 glmnet 0.909 0.875 0.966 0.966 0.977 0.989 0.966 0.966 0.966 0.943 0.955 0.966 0.955 0.955 0.989 PC-LDA 0.932 0.909 0.955 0.989 0.955 0.989 0.989 0.989 0.989 0.989 1 0.989 1 1 0.966 PC-QDA 0.92  0.943 0.966 0.955 0.966 0.966 0.977 0.977 0.977 0.966 0.966 0.966 0.955 0.966 0.932 RandomForest 0.92  0.92  0.989 0.989 0.989 0.989 0.989 0.989 0.966 0.966 0.966 0.966 0.966 0.966 0.966 RotForest 0.955 0.898 0.977 0.977 0.989 0.989 0.989 0.989 0.977 0.989 0.989 0.989 0.989 0.989 0.989 KNN 0.909 0.943 0.966 0.966 0.966 0.989 0.977 0.977 0.977 0.966 0.977 0.977 0.977 0.977 0.989 Ensemble3 0.909 0.886 0.977 0.989 0.977 0.989 0.989 0.989 0.966 0.966 0.966 0.966 0.977 0.966 0.977 Ensemble4 0.909 0.886 0.977 0.989 0.977 0.989 0.989 0.989 0.966 0.966 0.966 0.966 0.977 0.966 0.977 Ensemble5 0.909 0.886 0.977 0.977 0.977 0.989 0.989 0.977 0.966 0.966 0.966 0.966 0.966 0.966 0.977

TABLE 5E Classification accuracy CMS2 samples with Affymentrix dataset as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.88 0.875 0.936 0.941 0.949 0.939 0.946 0.949 0.949 0.952 0.962 0.954 0.964 0.964 0.962 SVM 0.855 0.883 0.929 0.946 0.944 0.959 0.962 0.969 0.952 0.969 0.964 0.972 0.967 0.969 0.964 DWD 0.765 0.804 0.906 0.901 0.893 0.908 0.916 0.929 0.921 0.929 0.934 0.929 0.944 0.949 0.952 glmnet 0.88 0.878 0.931 0.936 0.936 0.936 0.949 0.962 0.962 0.967 0.954 0.964 0.964 0.972 0.974 PC-LDA 0.878 0.885 0.946 0.954 0.954 0.962 0.954 0.959 0.977 0.974 0.972 0.974 0.974 0.977 0.974 PC-QDA 0.906 0.878 0.926 0.931 0.959 0.954 0.959 0.964 0.957 0.962 0.962 0.962 0.969 0.969 0.949 RandomForest 0.849 0.898 0.936 0.946 0.957 0.964 0.959 0.964 0.959 0.962 0.962 0.962 0.962 0.959 0.957 RotForest 0.893 0.913 0.934 0.944 0.957 0.952 0.959 0.967 0.964 0.957 0.969 0.959 0.957 0.967 0.959 KNN 0.872 0.901 0.959 0.964 0.972 0.977 0.982 0.967 0.972 0.977 0.982 0.987 0.987 0.98 0.974 Ensemble3 0.832 0.867 0.944 0.962 0.964 0.969 0.964 0.967 0.974 0.98  0.977 0.98  0.982 0.98 0.972 Ensemble4 0.834 0.867 0.944 0.962 0.964 0.969 0.964 0.967 0.974 0.98  0.977 0.98  0.982 0.98 0.972 Ensemble5 0.86 0.872 0.946 0.959 0.962 0.964 0.972 0.974 0.972 0.977 0.98  0.98  0.982 0.98 0.977

TABLE 6A Classification accuracy of CMS 3 samples on training data set V1 using 4-fold cross validation as function of number of genes. 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.716 0.705 0.903 0.915 0.915 0.909 0.909 0.926 0.932 0.949 0.943 0.915 0.926 0.92  0.909 SVM 0.466 0.585 0.881 0.869 0.869 0.903 0.898 0.898 0.903 0.909 0.915 0.915 0.92  0.915 0.875 DWD 0.648 0.693 0.903 0.898 0.92  0.915 0.903 0.92  0.932 0.955 0.943 0.949 0.938 0.932 0.915 glmnet 0.426 0.591 0.886 0.852 0.886 0.869 0.881 0.886 0.886 0.898 0.903 0.915 0.898 0.881 0.892 PC-LDA 0.443 0.58  0.784 0.807 0.847 0.864 0.875 0.892 0.852 0.875 0.864 0.858 0.869 0.869 0.807 PC-QDA 0.489 0.597 0.847 0.841 0.875 0.886 0.864 0.892 0.886 0.886 0.892 0.869 0.903 0.875 0.835 RandomForest 0.438 0.483 0.79  0.784 0.824 0.83  0.869 0.892 0.852 0.881 0.864 0.869 0.869 0.835 0.818 RotForest 0.472 0.472 0.773 0.784 0.79  0.818 0.795 0.835 0.841 0.886 0.858 0.886 0.881 0.881 0.852 KNN 0.432 0.534 0.778 0.801 0.801 0.835 0.881 0.864 0.909 0.903 0.881 0.864 0.892 0.864 0.699 Ensemble3 0.545 0.693 0.79  0.818 0.847 0.852 0.869 0.886 0.875 0.898 0.886 0.858 0.881 0.886 0.784 Ensemble4 0.534 0.693 0.801 0.807 0.847 0.852 0.864 0.886 0.875 0.898 0.886 0.858 0.881 0.886 0.801 Ensemble5 0.562 0.642 0.824 0.824 0.852 0.858 0.881 0.892 0.881 0.886 0.886 0.875 0.892 0.881 0.835

TABLE 6B Classification accuracy of CMS 3 samples on validation data set as function of number of genes. V2 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.478 0.551 0.758 0.775 0.775 0.792 0.831 0.848 0.882 0.899 0.893 0.916 0.916 0.899 0.904 SVM 0.455 0.528 0.742 0.758 0.781 0.798 0.803 0.826 0.826 0.854 0.86 0.86  0.871 0.871 0.865 DWD 0.607 0.674 0.831 0.843 0.837 0.876 0.871 0.904 0.904 0.91 0.921 0.921 0.927 0.916 0.904 glmnet 0.393 0.545 0.758 0.781 0.826 0.792 0.826 0.815 0.843 0.854 0.854 0.837 0.86  0.831 0.837 PC-LDA 0.416 0.522 0.702 0.708 0.736 0.747 0.787 0.815 0.831 0.82 0.809 0.826 0.843 0.826 0.736 PC-QDA 0.416 0.528 0.713 0.736 0.747 0.781 0.781 0.831 0.82  0.826 0.82 0.837 0.854 0.848 0.792 RandomForest 0.506 0.489 0.674 0.68  0.736 0.702 0.775 0.775 0.798 0.798 0.787 0.798 0.809 0.781 0.77  RotForest 0.382 0.365 0.596 0.612 0.635 0.669 0.691 0.725 0.781 0.809 0.787 0.831 0.837 0.792 0.792 KNN 0.416 0.5 0.691 0.697 0.702 0.753 0.792 0.815 0.82  0.792 0.787 0.826 0.815 0.809 0.68  Ensemble3 0.517 0.584 0.691 0.708 0.702 0.753 0.775 0.803 0.831 0.826 0.815 0.837 0.837 0.815 0.758 Ensemble4 0.511 0.579 0.691 0.708 0.702 0.753 0.775 0.809 0.831 0.831 0.815 0.837 0.837 0.82  0.764 Ensemble5 0.5 0.567 0.697 0.708 0.725 0.764 0.781 0.803 0.843 0.854 0.826 0.837 0.843 0.837 0.775

TABLE 6C Classification accuracy CMS3 samples w.th out of sample prediction set as function of number of genes, V2o 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 wSVM 0.32 0.48 0.72 0.76 0.68 0.76 0.8 0.96 0.96 0.92 0.92 0.92 0.92 0.92 SVM 0.4 0.48 0.72 0.76 0.72 0.76 0.76 0.88 0.88 0.92 0.92 0.92 0.92 0.92 DWD 0.6 0.72 0.88 0.88 0.8 0.88 0.96 0.96 0.96 0.96 0.96 0.92 0.96 0.96 glmnet 0.32 0.48 0.76 0.76 0.8 0.72 0.84 0.8 0.88 0.96 0.92 0.92 0.92 0.88 PC-LDA 0.44 0.52 0.68 0.68 0.76 0.76 0.88 0.8 0.84 0.84 0.84 0.88 0.88 0.84 PC-QDA 0.28 0.44 0.64 0.64 0.76 0.76 0.76 0.84 0.84 0.84 0.8 0.84 0.88 0.84 RandomForest 0.36 0.44 0.64 0.64 0.72 0.72 0.72 0.8 0.8 0.76 0.8 0.8 0.8 0.76 RotForest 0.28 0.28 0.4  0.48 0.6 0.6 0.6 0.72 0.76 0.84 0.8 0.84 0.84 0.8 KNN 0.28 0.32 0.6  0.68 0.64 0.68 0.72 0.8 0.8 0.76 0.84 0.88 0.84 0.84 Ensemble3 0.36 0.52 0.64 0.72 0.64 0.72 0.72 0.76 0.8 0.8 0.84 0.88 0.84 0.84 Ensemble4 0.36 0.48 0.64 0.72 0.64 0.72 0.72 0.76 0.8 0.8 0.84 0.88 0.84 0.84 Ensemble5 0.4 0.48 0.64 0.68 0.68 0.72 0.76 0.8 0.8 0.84 0.84 0.88 0.84 0.84

TABLE 6D Classification accuracy CMS 3 samples with TCGA dataset as function of number of genes. TCGA 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.385 0.462 0.731 0.731 0.769 0.846 0.885 0.846 0.846 0.923 0.885 0.923 0.923 0.885 0.885 SVM 0.423 0.462 0.731 0.769 0.769 0.846 0.846 0.846 0.769 0.846 0.846 0.846 0.846 0.808 0.846 DWD 0.577 0.577 0.769 0.808 0.846 0.885 0.885 0.885 0.885 0.846 0.923 0.923 0.923 0.885 0.846 glmnet 0.308 0.5 0.731 0.769 0.808 0.808 0.846 0.808 0.808 0.808 0.846 0.769 0.808 0.692 0.808 PC-LDA 0.423 0.5 0.692 0.654 0.615 0.692 0.769 0.731 0.769 0.769 0.769 0.808 0.808 0.731 0.731 PC-QDA 0.385 0.5 0.654 0.692 0.692 0.654 0.731 0.692 0.731 0.692 0.731 0.769 0.808 0.769 0.769 RandomForest 0.462 0.462 0.731 0.654 0.654 0.692 0.769 0.769 0.769 0.808 0.808 0.846 0.846 0.846 0.846 RotForest 0.346 0.5 0.538 0.538 0.577 0.692 0.769 0.731 0.731 0.692 0.808 0.885 0.846 0.808 0.654 KNN 0.462 0.423 0.654 0.731 0.731 0.808 0.846 0.846 0.846 0.769 0.731 0.808 0.769 0.769 0.731 Ensemble3 0.538 0.462 0.731 0.731 0.692 0.808 0.808 0.846 0.846 0.846 0.846 0.846 0.846 0.769 0.769 Ensemble4 0.5  0.462 0.731 0.731 0.692 0.808 0.808 0.846 0.846 0.846 0.846 0.846 0.846 0.769 0.769 Ensemble5 0.462 0.5 0.731 0.692 0.692 0.808 0.808 0.769 0.808 0.846 0.885 0.846 0.846 0.808 0.846

TABLE 6E Classification accuracy CMS3 samples with Affymetrix dataset as function of number of genes. V2a 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.5 0.576 0.754 0.788 0.788 0.822 0.847 0.898 0.932 0.932 0.924 0.949 0.949 0.932 0.924 SVM 0.483 0.534 0.746 0.771 0.797 0.831 0.831 0.864 0.881 0.898 0.907 0.924 0.924 0.924 0.89  DWD 0.644 0.695 0.856 0.873 0.856 0.898 0.915 0.924 0.941 0.941 0.941 0.949 0.949 0.949 0.924 glmnet 0.407 0.559 0.763 0.788 0.839 0.814 0.847 0.856 0.89  0.898 0.873 0.881 0.89  0.864 0.847 PC-LDA 0.432 0.551 0.703 0.72  0.771 0.771 0.814 0.839 0.881 0.89  0.873 0.881 0.89  0.881 0.771 PC-QDA 0.432 0.534 0.729 0.737 0.78 0.814 0.797 0.864 0.864 0.89  0.881 0.881 0.898 0.89  0.814 RandomForest 0.5 0.5 0.653 0.678 0.754 0.737 0.797 0.788 0.831 0.831 0.805 0.822 0.805 0.797 0.78  RotForest 0.398 0.339 0.593 0.619 0.661 0.669 0.686 0.754 0.822 0.864 0.847 0.873 0.89  0.847 0.856 KNN 0.407 0.492 0.703 0.703 0.72 0.788 0.814 0.856 0.881 0.864 0.864 0.898 0.873 0.864 0.754 Ensemble3 0.508 0.61 0.686 0.712 0.72 0.771 0.788 0.822 0.873 0.873 0.847 0.89  0.873 0.856 0.788 Ensemble4 0.508 0.602 0.686 0.712 0.72 0.771 0.788 0.831 0.873 0.881 0.847 0.89  0.873 0.864 0.797 Ensemble5 0.517 0.576 0.695 0.712 0.754 0.78  0.797 0.839 0.89  0.907 0.856 0.89  0.873 0.89  0.797

TABLE 7A Classification accuracy of CMS 4 samples on training data set V1 using 4-fold cross validation as function of number of genes. V1 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.818 0.872 0.923 0.955 0.946 0.938 0.94  0.946 0.94  0.949 0.957 0.963 0.966 0.957 0.952 SVM 0.847 0.875 0.912 0.923 0.929 0.92  0.92  0.943 0.935 0.952 0.943 0.946 0.952 0.952 0.94  DWD 0.832 0.852 0.923 0.94  0.943 0.943 0.957 0.952 0.94  0.952 0.949 0.955 0.946 0.949 0.943 glmnet 0.832 0.864 0.918 0.929 0.938 0.932 0.943 0.935 0.92  0.938 0.935 0.94  0.946 0.938 0.929 PC-LDA 0.821 0.838 0.858 0.886 0.889 0.875 0.889 0.884 0.875 0.889 0.892 0.895 0.881 0.892 0.852 PC-QDA 0.83  0.841 0.869 0.895 0.898 0.892 0.901 0.918 0.92  0.935 0.943 0.935 0.92  0.923 0.898 RandomForest 0.835 0.892 0.92  0.92  0.92  0.915 0.92  0.923 0.926 0.929 0.92  0.929 0.929 0.929 0.926 RotForest 0.821 0.895 0.915 0.929 0.912 0.94  0.938 0.949 0.94  0.946 0.943 0.957 0.96  0.96  0.94  KNN 0.807 0.824 0.866 0.875 0.872 0.881 0.886 0.895 0.892 0.886 0.878 0.878 0.878 0.889 0.824 Ensemble3 0.83  0.858 0.884 0.903 0.903 0.901 0.92  0.906 0.901 0.926 0.92  0.906 0.906 0.932 0.878 Ensemble4 0.832 0.861 0.884 0.906 0.903 0.903 0.92  0.909 0.903 0.926 0.92  0.906 0.906 0.932 0.878 Ensemble5 0.83  0.855 0.886 0.918 0.92  0.92  0.909 0.92  0.926 0.932 0.929 0.923 0.926 0.935 0.909

TABLE 7B Classification accuracy of CMS 4 samples on validation data set as function of number of genes. V2 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.845 0.863 0.909 0.927 0.942 0.944 0.95  0.936 0.947 0.959 0.968 0.971 0.971 0.977 0.977 SVM 0.857 0.871 0.906 0.93  0.933 0.944 0.95  0.947 0.956 0.956 0.95  0.968 0.977 0.974 0.965 DWD 0.868 0.883 0.901 0.936 0.927 0.942 0.953 0.947 0.953 0.965 0.956 0.956 0.95  0.956 0.968 glmnet 0.845 0.86  0.901 0.93  0.924 0.924 0.942 0.924 0.933 0.924 0.944 0.936 0.93  0.936 0.942 PC-LDA 0.822 0.825 0.848 0.889 0.898 0.895 0.898 0.892 0.901 0.912 0.906 0.904 0.906 0.901 0.909 PC-QDA 0.854 0.857 0.868 0.883 0.898 0.906 0.933 0.927 0.924 0.95  0.947 0.944 0.944 0.924 0.921 RandomForest 0.854 0.898 0.904 0.927 0.927 0.924 0.921 0.93  0.927 0.936 0.942 0.939 0.944 0.944 0.942 RotForest 0.845 0.868 0.895 0.915 0.921 0.933 0.933 0.942 0.936 0.959 0.956 0.959 0.962 0.965 0.939 KNN 0.833 0.836 0.863 0.901 0.88  0.871 0.895 0.909 0.909 0.906 0.918 0.933 0.936 0.936 0.877 Ensemble3 0.857 0.86  0.868 0.909 0.904 0.892 0.924 0.912 0.927 0.927 0.93  0.939 0.944 0.944 0.906 Ensemble4 0.857 0.863 0.868 0.912 0.904 0.892 0.924 0.912 0.927 0.93  0.936 0.942 0.944 0.944 0.906 Ensemble5 0.851 0.86  0.871 0.901 0.912 0.93  0.921 0.924 0.933 0.942 0.936 0.944 0.947 0.939 0.944

TABLE 7C Classification accuracy CMS 4 samples with out of sample prediction set as function of number of genes. V2o 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.75  0.776 0.868 0.908 0.895 0.908 0.934 0.934 0.947 0.934 0.947 0.961 0.974 0.974 0.987 SVM 0.75  0.75  0.855 0.868 0.895 0.934 0.961 0.947 0.947 0.934 0.934 0.961 0.987 0.987 0.987 DWD 0.776 0.776 0.829 0.882 0.882 0.934 0.947 0.934 0.947 0.947 0.947 0.934 0.921 0.934 0.974 glmnet 0.75  0.763 0.842 0.882 0.895 0.868 0.934 0.868 0.882 0.882 0.921 0.908 0.868 0.908 0.921 PC-LDA 0.724 0.697 0.816 0.882 0.895 0.895 0.855 0.868 0.868 0.895 0.868 0.868 0.895 0.882 0.882 PC-QDA 0.737 0.737 0.803 0.842 0.895 0.868 0.868 0.882 0.895 0.934 0.921 0.934 0.921 0.908 0.895 RandomForest 0.776 0.816 0.816 0.895 0.882 0.895 0.842 0.868 0.855 0.882 0.908 0.868 0.895 0.908 0.895 RotForest 0.737 0.776 0.868 0.908 0.895 0.908 0.908 0.934 0.921 0.934 0.934 0.934 0.947 0.947 0.908 KNN 0.737 0.75  0.803 0.934 0.908 0.895 0.895 0.921 0.895 0.882 0.908 0.908 0.934 0.921 0.842 Ensemble3 0.75  0.776 0.816 0.921 0.908 0.895 0.882 0.895 0.882 0.882 0.895 0.895 0.934 0.921 0.882 Ensemble4 0.75  0.776 0.816 0.934 0.908 0.895 0.882 0.895 0.882 0.882 0.908 0.908 0.934 0.921 0.882 Ensemble5 0.75  0.75  0.816 0.921 0.908 0.921 0.868 0.882 0.908 0.908 0.908 0.921 0.934 0.921 0.921

TABLE 7D Classification accuracy CMS 3 samples with TCGA dataset as function of number of genes. TCGA 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.826 0.848 0.913 0.891 0.935 0.891 0.913 0.913 0.935 0.978 0.978 0.978 0.957 0.978 0.957 SVM 0.826 0.848 0.913 0.957 0.913 0.891 0.913 0.913 0.935 0.935 0.935 0.935 0.957 0.957 0.935 DWD 0.87  0.891 0.913 0.957 0.935 0.913 0.913 0.935 0.935 0.957 0.935 0.957 0.935 0.957 0.957 glmnet 0.826 0.804 0.935 0.913 0.891 0.891 0.87  0.891 0.957 0.935 0.935 0.913 0.935 0.913 0.913 PC-LDA 0.761 0.761 0.848 0.848 0.826 0.848 0.826 0.761 0.848 0.87  0.87  0.891 0.87  0.891 0.913 PC-QDA 0.848 0.848 0.891 0.826 0.848 0.891 0.935 0.891 0.891 0.935 0.935 0.913 0.935 0.87  0.848 RandomForest 0.848 0.826 0.826 0.848 0.826 0.848 0.87  0.848 0.87  0.87  0.87  0.891 0.891 0.891 0.87  RotForest 0.848 0.848 0.913 0.891 0.848 0.891 0.891 0.891 0.891 0.978 0.935 0.978 0.957 0.978 0.913 KNN 0.804 0.761 0.87  0.87  0.804 0.783 0.761 0.804 0.826 0.826 0.848 0.87  0.87  0.848 0.804 Ensemble3 0.848 0.826 0.848 0.848 0.848 0.826 0.848 0.783 0.87  0.87  0.891 0.913 0.891 0.891 0.848 Ensemble4 0.848 0.848 0.848 0.848 0.848 0.826 0.848 0.783 0.87  0.87  0.891 0.913 0.891 0.891 0.848 Ensemble5 0.826 0.848 0.891 0.848 0.848 0.891 0.87  0.848 0.913 0.891 0.913 0.913 0.913 0.913 0.891

TABLE 7E Classification accuracy of CMS 3 samples with Affymetrix data set as function of number of genes. V2a 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.5  0.576 0.754 0.788 0.788 0.822 0.847 0.898 0.932 0.932 0.924 0.949 0.949 0.932 0.924 SVM 0.483 0.534 0.746 0.771 0.797 0.831 0.831 0.864 0.881 0.898 0.907 0.924 0.924 0.924 0.89  DWD 0.644 0.695 0.856 0.873 0.856 0.898 0.915 0.924 0.941 0.941 0.941 0.949 0.949 0.949 0.924 glmnet 0.407 0.559 0.763 0.788 0.839 0.814 0.847 0.856 0.89  0.898 0.873 0.881 0.89  0.864 0.847 PC-LDA 0.432 0.551 0.703 0.72  0.771 0.771 0.814 0.839 0.881 0.89  0.873 0.881 0.89  0.881 0.771 PC-QDA 0.432 0.534 0.729 0.737 0.78  0.814 0.797 0.864 0.864 0.89  0.881 0.881 0.898 0.89  0.814 RandomForest 0.5  0.5  0.653 0.678 0.754 0.737 0.797 0.788 0.831 0.831 0.805 0.822 0.805 0.797 0.78  RotForest 0.398 0.339 0.593 0.619 0.661 0.669 0.686 0.754 0.822 0.864 0.847 0.873 0.89  0.847 0.856 KNN 0.407 0.492 0.703 0.703 0.72  0.788 0.814 0.856 0.881 0.864 0.864 0.898 0.873 0.864 0.754 Ensemble3 0.508 0.61  0.686 0.712 0.72  0.771 0.788 0.822 0.873 0.873 0.847 0.89  0.873 0.856 0.788 Ensemble4 0.508 0.602 0.686 0.712 0.72  0.771 0.788 0.831 0.873 0.881 0.847 0.89  0.873 0.864 0.797 Ensemble5 0.517 0.576 0.695 0.712 0.754 0.78  0.797 0.839 0.89  0.907 0.856 0.89  0.873 0.89  0.797

Comparison of Methods on CRC Data Set, Validation Data Set V2: The wSVM was selected based on the 4-fold CV performance in the training data V1, and its accuracy was assessed on the independent data set V2, plus the subsets to assess out of sample performance (V2o), RNAseq performance (TCGA), and Affymetrix sample performance (V2a). Tables 8A-D present the classification accuracies for these validation data sets for the wSVM for the various model sizes, and also includes for comparison the validation performance of the other methods considered on V1. FIG. 2 plots the 4-group classification accuracies of each method on the full validation data (V2) as a function of the model size.

The wSVM maintained outstanding performance for the validation data set (V2) that is on par with the 4-fold CV results for V1, with similar accuracies for the 5,973 gene model (0.955 for V2 vs. 0.959 for V1), the 500 gene model (0.959 vs. 0.963), the 75 gene model (0.932 vs. 0.950), and the 20 gene model (0.898 vs. 0.920). Once again, a bump in performance was seen for considering models of size smaller than 5,973, and the wSVM along with SVM and DWD appear to be best in the validation data, as well, supporting the choice to focus on the wSVM classifier.

The performance of the wSVM classifier for the out-of-sample subset (V2o), the RNAseq subset (TCGA), and the Affymetrix subset (V2a), were comparable to the overall validation performance (V2), with the 500 gene model having 4-group classification accuracy of 0.967 for V2o, 0.963 for TCGA, and 0.959 for V2a compared with 0.959 for the entire V2 data set. This suggests that the classifier is robust to platform (Affymetrix vs. RNAseq) and has good out-of-sample performance in the context of the CRCSC data sets.

The validation performance also looks good across CMS (see Tables 4A-7E for 4-group classification accuracy in validation data and its subsets). The 500-gene wSVM has classification accuracies of 0.940, 0.972, 0.916, and 0.971 for CMS1-4, respectively.

Tables 8A-D. Classification accuracy on validation data set. the entire set (V2; Table 8A), out-of-sample prediction (V2o; Table 8B). Affymetrix subset (V2a: Table 8C). and TCGA RNAseq subset (TCGA. Table 8D) for various methods as function of number of genes.

TABLE 8A V2 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.784 0.813 0.898 0.906 0.913 0.920 0.929 0.932 0.937 0.947 0.956 0.955 0.959 0.959 0.955 SVM 0.763 0.818 0.889 0.906 0.908 0.926 0.927 0.938 0.932 0.947 0.947 0.953 0.954 0.953 0.95  DWD 0.750 0.797 0.885 0.899 0.898 0.919 0.924 0.933 0.932 0.941 0.944 0.946 0.950 0.951 0.953 glmnet 0.763 0.812 0.885 0.902 0.907 0.911 0.926 0.923 0.924 0.929 0.934 0.929 0.932 0.934 0.94  PC-LDA 0.761 0.805 0.876 0.891 0.894 0.902 0.907 0.914 0.927 0.929 0.927 0.934 0.938 0.932 0.918 PC-QDA 0.780 0.809 0.874 0.885 0.898 0.912 0.920 0.926 0.922 0.934 0.933 0.938 0.941 0.932 0.91  RandomForest 0.773 0.821 0.884 0.895 0.907 0.906 0.916 0.921 0.917 0.925 0.925 0.927 0.931 0.924 0.924 RotForest 0.769 0.802 0.869 0.883 0.897 0.901 0.91  0.919 0.926 0.936 0.941 0.944 0.939 0.94  0.928 KNN 0.769 0.814 0.878 0.889 0.888 0.9  0.913 0.918 0.916 0.912 0.92  0.929 0.927 0.921 0.865 Ensemble3 0.772 0.813 0.880 0.898 0.899 0.905 0.92  0.921 0.930 0.934 0.934 0.941 0.943 0.937 0.909 Ensemble4 0.772 0.813 0.880 0.90  0.901 0.905 0.919 0.922 0.930 0.936 0.935 0.943 0.943 0.938 0.914 Ensemble5 0.777 0.813 0.882 0.898 0.904 0.916 0.922 0.926 0.932 0.943 0.941 0.946 0.947 0.942 0.935

TABLE 8B V2o 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.749 0.782 0.874 0.887 0.891 0.904 0.912 0.941 0.946 0.937 0.958 0.958 0.967 0.962 0.958 SVM 0.736 0.778 0.858 0.887 0.895 0.921 0.921 0.950 0.929 0.954 0.954 0.967 0.971 0.967 0.962 DWD 0.720 0.782 0.858 0.887 0.883 0.912 0.929 0.929 0.925 0.941 0.954 0.946 0.954 0.958 0.967 glmnet 0.732 0.782 0.854 0.874 0.891 0.879 0.921 0.904 0.900 0.925 0.937 0.925 0.916 0.950 0.950 PC-LDA 0.736 0.770 0.866 0.904 0.916 0.916 0.891 0.908 0.921 0.929 0.921 0.933 0.946 0.929 0.921 PC-QDA 0.736 0.770 0.849 0.870 0.912 0.900 0.900 0.921 0.921 0.941 0.937 0.954 0.950 0.941 0.925 RandomForest 0.745 0.791 0.866 0.895 0.900 0.908 0.900 0.921 0.916 0.921 0.937 0.929 0.933 0.933 0.929 RotForest 0.732 0.774 0.858 0.883 0.904 0.895 0.908 0.937 0.921 0.941 0.946 0.946 0.946 0.946 0.925 KNN 0.728 0.774 0.854 0.895 0.900 0.900 0.908 0.908 0.912 0.895 0.921 0.929 0.929 0.925 0.845 Ensemble3 0.724 0.778 0.862 0.908 0.904 0.908 0.916 0.912 0.925 0.916 0.941 0.946 0.954 0.950 0.916 Ensemble4 0.728 0.774 0.866 0.912 0.908 0.908 0.916 0.912 0.925 0.916 0.946 0.950 0.954 0.950 0.921 Ensemble5 0.749 0.770 0.866 0.908 0.912 0.912 0.912 0.916 0.925 0.929 0.946 0.954 0.954 0.950 0.950

TABLE 8C V2a 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.793 0.822 0.898 0.911 0.913 0.922 0.930 0.943 0.941 0.944 0.959 0.955 0.959 0.959 0.958 SVM 0.773 0.823 0.890 0.910 0.914 0.933 0.936 0.948 0.938 0.953 0.950 0.962 0.960 0.958 0.955 DWD 0.763 0.809 0.891 0.901 0.897 0.916 0.926 0.934 0.931 0.942 0.943 0.942 0.945 0.948 0.954 glmnet 0.775 0.820 0.885 0.903 0.907 0.913 0.929 0.935 0.930 0.938 0.940 0.938 0.936 0.946 0.943 PC-LDA 0.769 0.815 0.879 0.897 0.905 0.909 0.906 0.922 0.932 0.938 0.932 0.938 0.941 0.936 0.925 PC-QDA 0.787 0.812 0.879 0.888 0.909 0.920 0.925 0.930 0.924 0.939 0.939 0.944 0.945 0.939 0.920 RandomForest 0.775 0.826 0.879 0.895 0.914 0.910 0.916 0.926 0.924 0.930 0.931 0.930 0.929 0.926 0.927 RotForest 0.771 0.804 0.870 0.885 0.903 0.902 0.912 0.927 0.930 0.938 0.945 0.942 0.936 0.941 0.933 KNN 0.774 0.820 0.885 0.898 0.900 0.914 0.926 0.931 0.929 0.928 0.935 0.939 0.936 0.932 0.888 Ensemble3 0.775 0.822 0.881 0.902 0.907 0.911 0.924 0.930 0.936 0.943 0.945 0.947 0.948 0.947 0.920 Ensemble4 0.776 0.821 0.882 0.904 0.910 0.911 0.924 0.931 0.936 0.945 0.946 0.949 0.948 0.949 0.924 Ensemble5 0.784 0.818 0.882 0.903 0.912 0.917 0.927 0.936 0.939 0.950 0.945 0.953 0.952 0.952 0.940

TABLE 8D TCGA 5 10 20 25 30 40 50 75 100 200 300 400 500 1000 5973 wSVM 0.820 0.820 0.926 0.926 0.947 0.947 0.947 0.947 0.947 0.968 0.958 0.963 0.963 0.968 0.958 SVM 0.804 0.841 0.921 0.942 0.931 0.942 0.937 0.947 0.937 0.947 0.952 0.952 0.952 0.952 0.952 DWD 0.794 0.794 0.915 0.937 0.947 0.952 0.947 0.952 0.952 0.947 0.947 0.963 0.958 0.963 0.958 glmnet 0.799 0.810 0.931 0.931 0.937 0.942 0.931 0.926 0.942 0.931 0.937 0.921 0.926 0.905 0.942 PC-LDA 0.810 0.825 0.899 0.910 0.884 0.915 0.921 0.899 0.926 0.931 0.937 0.942 0.942 0.937 0.926 PC-QDA 0.825 0.852 0.910 0.894 0.905 0.910 0.937 0.921 0.926 0.926 0.931 0.931 0.937 0.915 0.894 RandomForest 0.831 0.831 0.910 0.905 0.899 0.915 0.931 0.921 0.915 0.921 0.915 0.926 0.926 0.921 0.915 RotForest 0.836 0.831 0.899 0.899 0.899 0.926 0.937 0.931 0.926 0.942 0.947 0.974 0.963 0.963 0.921 KNN 0.804 0.825 0.899 0.91  0.889 0.899 0.905 0.915 0.915 0.899 0.905 0.921 0.921 0.910 0.889 Ensemble3 0.836 0.815 0.915 0.921 0.910 0.926 0.931 0.921 0.931 0.931 0.926 0.937 0.937 0.915 0.915 Ensemble4 0.831 0.820 0.915 0.921 0.910 0.926 0.931 0.921 0.931 0.931 0.926 0.937 0.937 0.915 0.915 Ensemble5 0.820 0.825 0.926 0.910 0.910 0.942 0.937 0.921 0.937 0.937 0.947 0.942 0.937 0.931 0.937

Characterizing Performance of 472-gene wSVM Classifier: As described in the methods, the wSVM classifier with 472 genes was chosen to move forward in the context of FFPE samples using the Nanostring platform. This classifier yielded an overall 96.3% classification accuracy in the Affymetrix subset of V2, with 96.6%, 96.7%, 93.2%, and 97.1% accuracy for CMS1, CMS2, CMS3, and CMS4, respectively, in this subset. The CMS structure in this gene set is remarkably persistent, being highly consistent in the training (V1) and validation (V2ap, Affymetrix subset with fRMA probe-level data) data sets (see heat maps in FIG. 1).

Comparison with classifiers in Guinney et al. (2015): Guinney et al. (2015) presented two CMS classifiers: a random forest classifier (RF) using 5,973 genes and a “single sample” classifier (SSP) based on nearest centroid predictor applied using 693 genes. The paper reported classification results for a set of 3,104 samples with “gold standard” consensus CMS class, but these included both training and validation samples and are based on the approach of not forcing a single CMS call for each samples, but only providing classifications for samples with high confidence, defined as posterior probability>0.50 for RF and high correlation (>0.15) with nearest centroid and correlation much less (at least 0.06) for the next closest centroid for SSP, respectively. This criteria left 429/3104 “undetermined” for RF and 666/3104 “undetermined” for SSP. To obtain a more fair comparison with our classifier, our 472-gene wSVM classifier was compared to RF and SSP on the validation data set V2 containing 1,329 samples in two ways: (1) forcing CMS calls on all samples for each method, and (2) calling CMS only for samples with “high confidence,” using the criteria in Guinney et al. (2015) for RF and SSP, and maximum class probability α_(i)>0.80 for the wSVM. Results are presented in Table 9. The wSVM yielded higher classification accuracy than the RF or SSP when forcing CMS calls for all samples. When only calling CMS when confidence is high, the wSVM classifies slightly more samples with high confidence (1186/1329=0.892 vs. 1177/1329=0.886 for RF and 1148/1329=0.864 for SSP) yet also has higher classification accuracy (0.981 vs. 0.968 for RF and 0.970 for SSP). Note that the RF and SSP were trained using all data (including V1 and V2), while our wSVM was only trained on V1, so these V2 results are technically independent validation samples for the wSVM but not RF and SSP. Altogether, the wSVM, although using fewer genes, yields improved classification accuracy relative to the RF and SSP in Guinney et al. (2015) for the CRCSC validation data set V2.

TABLE 9 Comparison of Classifiers. Comparison of random forest (RF) and single sample predictors (SSP) in Guinney et al. (2015) with the wSVM classifier with 472 genes, either forcing a single CMS call for each sample (all calls) or only when classified with high confidence (high probability). RF SSP wSVM All calls Number of samples 1329 1329 1329 Predictive accuracy 0.942 0.932 0.959 High confidence Number of samples 1177 1148 1186 Proportion classified 0.886 0.864 0.892 Predictive accuracy 0.968 0.970 0.981

Performance by Confidence: The top panel of FIG. 3 plots the distribution of maximum class probabilities α_(i) (i.e. classification confidence) for the 472-gene wSVM on the 713 Affymetrix validation samples with fRMA probe-level values (V2ap), and the bottom panel plots the classification accuracy (1=correct, 0=incorrect) vs. the maximum class probability of the 472-gene wSVM classifier along with a loess fit of the predictive accuracy as a function of a. From the top, 75%-80% of samples had classification confidence of α_(i)>0.90, and among these there were only two misclassifications (>99.6% accuracy), and >90% of samples had a classification confidence of α_(i)>0.80, with only 8 misclassifications (>98.8% accuracy), suggesting that samples classified into a CMS with high confidence by our 472-gene wSVM classifier were virtually always correct in the validation data. Table 10 shows all of the misclassified samples along with the corresponding wSVM class probabilities (π_(ij)) for each CMS, classification confidence (α_(i)), and an indication of whether this sample could be considered a “CMS mixture” (i.e. π_(ij)>0.20 for multiple CMS) and if the “gold standard” was a part of that mixture. Most of the “misclassified samples” had lower classification confidence α_(i), and many had evidence of being CMS mixtures, with the “gold standard” CMS being a component of the mixtures.

TABLE 10 Validation samples misclassified by 472-gene wSVM classifier: Gold standard CMS, predicted CMS, the class probabilities π_(ij) for each CMS (probability for gold standard in bold), and maximum probability α_(i) for validation samples (V1ap) misclassified by 472- gene wSVM. Note that in many cases, the gold standard CMS, while not highest, has high probability, and in many of these cases there is evidence of a CMS mixture, given multiple CMS with non-negligible (>0.20) probabilities, and if that mixture includes the “gold standard” CMS. Gold Predicted CMS CMS Mixture Standard CMS Confidence Include “Consensus” (wSVM- CMS Class probabilities π_(ij) α_(i) = multiple gold CMS 472) CMS1 CMS2 CMS3 CMS4 max (π_(ij)) π_(ij) > 0.20 standard? CMS4 CMS1 0.36 0.27 0.02 0.35 0.36 yes yes CMS3 CMS2 0.01 0.50 0.36 0.13 0.50 yes yes CMS4 CMS2 0.00 0.51 0.00 0.48 0.51 yes yes CMS2 CMS1 0.53 0.40 0.05 0.01 0.53 yes yes CMS4 CMS2 0.00 0.61 0.03 0.36 0.61 yes yes CMS1 CMS3 0.38 0.00 0.61 0.00 0.61 yes yes CMS4 CMS1 0.65 0.00 0.00 0.35 0.65 yes yes CMS2 CMS4 0.01 0.32 0.01 0.66 0.66 yes yes CMS1 CMS4 0.25 0.03 0.06 0.66 0.66 yes yes CMS3 CMS2 0.00 0.67 0.33 0.00 0.67 yes yes CMS2 CMS3 0.01 0.29 0.69 0.01 0.69 yes yes CMS2 CMS4 0.00 0.29 0.00 0.71 0.71 yes yes CMS4 CMS2 0.00 0.73 0.00 0.27 0.73 yes yes CMS1 CMS3 0.19 0.07 0.74 0.00 0.74 no no CMS4 CMS1 0.78 0.00 0.00 0.22 0.78 yes yes CMS4 CMS1 0.79 0.00 0.00 0.21 0.79 yes yes CMS3 CMS1 0.80 0.09 0.11 0.00 0.80 no no CMS3 CMS1 0.80 0.01 0.19 0.00 0.80 no no CMS2 CMS4 0.00 0.18 0.00 0.81 0.81 no no CMS2 CMS3 0.03 0.13 0.83 0.02 0.83 no no CMS3 CMS2 0.03 0.85 0.13 0.00 0.85 no no CMS1 CMS3 0.15 0.00 0.85 0.00 0.85 no no CMS3 CMS1 0.91 0.00 0.08 0.00 0.91 no no CMS1 CMS3 0.02 0.00 0.97 0.01 0.97 no no

Performance of 472-gene CRCSC Classifier Based on Single Affymetrix Probe: As mentioned above, the Nanostring assay was designed based on a 472-gene signature, with code sets chosen to match the single Affymetrix probe for each gene best recapitulating the gene-level expressions. After quantifying expression of these genes based on these single probes and applying quantile normalization to these quantifications, the wSVM classifier was retrained for data set V1 to yield a single-probe Affymetrix FF-based classifier, and then assessed for its performance on CRCSC validation data V2ap. Good accuracy was observed in the CRCSC validation data when forcing each sample to have a single CMS class (89.0%), and for the validation samples with high classification confidence (π_(i)>90%), the accuracy was even better (97.4%). The use of a single probe per gene was sufficient to obtain good CMS classification results in the setting of Affymetrix data on batch-corrected FF samples.

Pilot study of Technical Variability: Table 11 contains the 6-number summary (minimum, maximum, Q25, Q75, mean, median) for percent biological variability (PBV) and coefficient of variation (CV) across the 472 signature genes on the Nanostring array for the 6 FFPE samples run in duplicate on the Nanostring assay. The technical replicate variability was negligible relative to biological variability, with half of the genes with >95.9% biological variability and 75% of the genes having >86.8% biological. The median CV was <0.04, with range of 0.00-0.24 and interquartile range of 0.01-0.07, further demonstrating excellent technical reproducibility within runs.

TABLE 11 Nanostring Pilot Study. Summary of percent of biological variability (PBV) and coefficient of variation (CV) across the 472 signature genes on Nanostring assay from 6 duplicate FFPE samples. Min Q25 Median Mean Q75 Max PBV 0 86.84 95.92 87.76 98.96 100 CV 0 0.013 0.038 0.049 0.072 0.241

Nanostring, FFPE Classifier: As mentioned above, the strategy for building the Nanostring FFPE-based classifier was to first find a subset of the 472 genes with high FF/FFPE correlation, and then train the wSVM classifier in the CRCSC training data set V1, using quantile normalization over this reduced gene set. The performance of this classifier was validated on the 85 FFPE samples from the CRCSC cohort and the 73 FFPE samples not from the CRCSC cohort after applying sample-specific quantile normalization. Note that since the classifier is trained on the CRCSC training data set V1, and the genes selected not based on classification accuracy but based on FF/FFPE correlation, the subsequent classification accuracies of the 158 FFPE samples serve as validation measurements and are appropriate to directly compute without using cross-validation.

Correlation of FF/FFPE: Sample-specific Spearman correlation of paired FF/FFPE samples was first computed across the 472 CMS genes on the Nanostring assay. FIG. 4A contains a histogram of these correlations, and Table 12 contains a summary of this distribution across all samples, as well as split out by the two batches (85 CRCSC samples and 73 non-CRCSC samples). For most samples, the FF/FFPE correlation is very high, but for a small subset it is lower. Samples with low sample-wise FF/FFPE correlation tended to have poorer RNA quality for their FF samples (p=0.0077), but there was little association with FFPE RNA quality (p=0.28 with %200 nt) (FIGS. 5A&B).

TABLE 12 Summary of sample-wise FF/FFPE Spearman Correlations. Summary of sample- wise paired FF/FFPE correlations across 472 CMS genes for batch 1 containing CRCSC samples (top), batch 2 containing non-CRCSC samples (middle), and all samples (bottom). Q05 Q25 Median Q75 Q95 R > 0.75 Batch 1, N = 76 (CRCSC) 0.616 0.791 0.838 0.887 0.913 0.816 Batch 2, N = 104 (non-CRCSC) 0.552 0.758 0.854 0.892 0.921 0.769 All Samples 0.580 0.774 0.849 0.890 0.916 0.789

To select a subset of genes with high FF/FFPE correlation to use with our CMS classifier, gene-specific Spearman correlations were computed for all 472 CMS genes for paired FF/FFPE samples across all subjects with FF/FFPE correlations of at least 0.75. FIG. 4B contains a histogram of these correlations, and the top rows of Table 13 summarize this distribution, both overall and split out by the two batches. There is a sizable number of genes with reasonably high FF/FFPE correlation (>0.60), and some genes with very low correlation for which the paired FFPE and FF measurements are strongly discordant. The top 100 genes in terms of FF/FFPE correlation were selected, and the bottom rows of Table 13 summarize the distributions for these genes overall and split out by batches. FIG. 6 plots the Spearman correlations of all 472 Nanostring genes in batch 1 vs. batch 2, with the overall top 100 plotted in red, the next 100 plotted in blue, and the rest in black. Note the high level of agreement across batches, that genes with high FF/FFPE correlations for one batch tended to also have high FF/FFPE correlation in the other batch, and genes with very poor agreement in one batch also had poor agreement in the other batch. This suggests that, as hypothesized, high FF/FFPE correlation is a characteristic of the gene/probe set, and thus it is important to focus on subsets of genes with high FF/FFPE correlation in order to accurately classify FFPE samples. The top 100 genes in terms of FF/FFPE correlation were chosen for the FFPE, Nanostring-based classifier.

TABLE 13 Summary of gene-wise FF/FFPE Spearman Correlations. Summary of gene-wise paired FF/FFPE Spearman Correlations for all 472 genes (top) and top 100 genes (bottom), computed using samples with sample-wise FF/FFPE correlation of at least 0.75, summarized by batch and overall. Gene Set Q05 Q25 Median Q75 Q95 All 472 Batch 1, N = 76 (CRCSC) 0.204 0.367 0.510 0.613 0.717 Batch 2, N = 104 (non-CRCSC) 0.290 0.505 0.630 0.730 0.825 All Samples 0.254 0.435 0.577 0.669 0.767 Top 100 Batch 1, N = 76 (CRCSC) 0.538 0.616 0.660 0.714 0.782 Batch 2, N = 104 (non-CRCSC) 0.633 0.722 0.772 0.815 0.845 All Samples 0.634 0.683 0.730 0.766 0.809

Nanostring FFPE Classifier Performance: Choosing this subset of 100/472=21.2% of the genes with strong evidence of high FF/FFPE correlation, the wSVM classifier was retrained on the CRCSC training data set V1, doing subject-specific quantile normalization across this set of 100 genes. This 100-gene wSVM classifier was then directly applied to the 158 FFPE samples run on the Nanostring platform after quantile normalization to transform the Nanostring expressions onto the scale of the CRCSC data. Classification accuracy is given in the second columns of Tables 14A-D, comparing with the gold standard CMS for CRCSC subjects (N=85) and comparing to the 472 gene classifier applied to Affymetrix runs on matched FF samples for patients in the non-CRCSC cohort (N=73). In the latter case, the Affymetrix-FF runs were considered to be pseudo-gold standard given the exceptional performance for the 472 gene FF classifier applied to Affymetrix samples reported above.

The 100 gene model applied to FFPE samples had 4-group accuracy of 0.797 (126/158), with 0.812 (69/85) for CRCSC samples and 0.781 (57/73) for non-CRCSC samples. For samples classified with high confidence (α_(i)>0.80 or 0.90), the performance was better with 4-group accuracy of 0.858 and 0.889, respectively. Applied to the FF samples, this 100-gene model had 4-group classification accuracy of 0.804 (123/153), with 0.738 (59/80) for CRCSC samples and 0.877 (64/73) for non-CRCSC samples, with 4-class accuracy of 0.868 and 0.924 for samples classified with high confidence π_(i)=0.80 or 0.90, respectively. FIGS. 7A&B plot the 4-class accuracy vs. confidence level π_(i) for FFPE and FF samples, demonstrating that samples classified with high confidence were more likely to be accurately classified. FIGS. 7C&D plot the 4-class accuracy vs. RNA quality, defined by %200 nt (FFPE) or RIN (FF), demonstrating that there is little if any association of CMS accuracy with RNA quality, suggesting that the performance of classifier is robust to RNA quality in this study. These results together suggest that the 100-gene Nanostring-based classifier performs reasonably well for either FFPE or FF samples.

Tables 14A-D. CMS Classifier Accuracy: 4-class accuracy of CMS classifiers, along with number (proportion) of samples classified to each CMS. The accuracy for the classifier with the top 100 genes was assessed in terms of FF/FFPE correlation for FFPE and FF, computed based on Nanostring measurements for FFPE and FF (Nano FFPE-100, Nano FF-100) and based on Affymetrix measurements for FF in the Affy CRCSC validation data set (V2a, Affy FF-100), and for the full 472 gene classifier applied to FF samples run on the Nanostring platform (Nano FF-472) and FF samples run on Affymetrix in the Affymetrix CRCSC validation data set (Affy FF-472). Performance is summarized overall (Table 14A) and for subsets of samples with high classification confidence (a, >0.50 (Table 14B), 0.80 (Table 14C), or 0.90 (Table 14D)).

TABLE 14A No 4-class Distribution of predicted CMS threshold accuracy CMS1 CMS2 CMS3 CMS4 Nano 0.797 38 (0.24) 88 (0.56)  9 (0.06) 23 (0.15) FFPE-100 Nano 0.804 34 (0.22) 72 (0.47) 31 (0.20) 16 (0.10) FF-100 Nano 0.810 40 (0.26) 64 (0.42) 20 (0.13) 29 (0.19) FF-472 Affy 0.892 222 597 (0.45)  176 334 (0.25)  FF-100* (0.17) (0.13) Affy 0.953 232 572 (0.43)  173 352 (0.27)  FF-472* (0.18) (0.13)

TABLE 14B 4-class Distribution of predicted CMS P > 0.50 accuracy CMS1 CMS2 CMS3 CMS4 unclassified Nano FFPE-100 0.808  36 (0.23)  85 (0.54)   8 (0.05)  22 (0.14)  5 (0.03) Nano FF-100 0.823  34 (0.22)  71 (0.46)  28 (0.18)  19 (0.12)  4 (0.03) Nano FF-472 0.819  38 (0.25)  60 (0.40)  19 (0.13)  27 (0.18)  7 (0.05) Affy FF-100* 0.897 218 (0.16) 590 (0.44) 172 (0.13) 329 (0.25) 20 (0.02) Affy FF-472* 0.954 231 (0.17) 569 (0.43) 170 (0.13) 348 (0.26) 11 (0.01)

TABLE 14C 4-class Distribution of predicted CMS P > 0.80 accuracy CMS1 CMS2 CMS3 CMS4 unclassified Nano FFPE-100 0.858  29 (0.19)  65 (0.42)   8 (0.05)  11 (0.07)  43 (0.28) Nano FF-100 0.868  29 (0.19)  64 (0.42)  19 (0.13)   9 (0.06)  30 (0.20) Nano FF-472 0.851  38 (0.25)  60 (0.40)  19 (0.13)  27 (0.18)   7 (0.05) Affy FF-100* 0.960 162 (0.12) 479 (0.36) 120 (0.09) 248 (0.19) 320 (0.24) Affy FF-472* 0.982 190 (0.14) 530 (0.40) 146 (0.11) 313 (0.24) 150 (0.11)

TABLE 14D 4-class Distribution of predicted CMS P > 0.90 accuracy CMS1 CMS2 CMS3 CMS4 unclassified Nano FFPE-100 0.889  25 (0.16)  51 (0.33)   7 (0.04)   7 (0.04)  66 (0.42) Nano FF-100 0.924  20 (0.13)  51 (0.34)  16 (0.11)   5 (0.03)  59 (0.39) Nano FF-472 0.883  30 (0.20)  42 (0.28)  12 (0.08)  20 (0.13)  48 (0.32) Affy FF-100* 0.980 131 (0.10) 418 (0.31) 103 (0.08) 199 (0.15) 478 (0.36) Affy FF-472* 0.998 169 (0.13) 498 (0.38) 123 (0.09) 288 (0.22) 251 (0.19)

Validation of the CMS Assay at the research molecular diagnostic laboratory: The CRC CMS-100 assay was 100% reproducible in predicting a CMS subtype across different runs (12 samples=48 runs), between two laboratory personnel (12 samples) and with different RNA input concentration (n=6). The reproducibility between biopsy and resection was 91% with 15 of 17 patients had same CMS subtype between matched biopsy and resection specimens (Table 15).

TABLE 15 Validation of CMS assay at the research molecular diagnostic laboratory Colon Resection Run 5 RNA RNA RNA RNA Separate input input input input Run by Matching Run Run Run Run (500 (250 (100 (50 Technician Colonoscopic Sample ID 1 2 3 4 ng) ng) ng) ng) #2 Biopsy MTDL 207 2 2 2 2 2 2 2 2 2 2 MTDL 228 1 1 1 1 1 1 1 1 1 MTDL 214 2 2 2 2 2 2 2 2 2 2 MTDL999 1 1 1 1 1 MTDL202 2 2 2 2 2 MTDL234 2 2 2 2 2 MTDL 1148 4 4 4 4 4 MTDL 232 1 1 1 1 1 MTDL 77 2 2 2 2 2 MTDL 997 4 4 4 4 4 MTDL 192 2 2 2 2 2 2 MTDL 76 M M M M M MTDL 411 2 2 2 2 2 2 MTDL 1001 4 4 4 4 4 MTDL 72 1 1 1 1 1 MTDL 87 2 2 MTDL 211 2 2 MTDL 221 2 2 MTDL 83 1 1 MTDL 982 2 2 MTDL 993 1 1 MTDL 941 1 1 MTDL 998 4 4 MTDL 954 4 M MTDL 226 2 4

Performance of CRC CMS-200 in CLIA-certified Molecular Diagnostic Laboratory: Thirty-two out of 35 samples were accurately assigned the CMS subtype from the initial results as compared to the gold standard results (CRCSC subtype). The 3 discordant cases were not included in the overall accuracy as all had probability for a CMS subtype was in borderline range (>0.43<0.57). These samples were considered to have “mixed consensus” molecular subtypes and were reported as such. The reproducibility of the assay was determined by review of CMS calls for 24 samples that were run across 11 runs generating 120 separate reactions. One hundred and thirteen of 120 (94%) had expected results and correct CMS call. The average standard deviation of the gene expression score across all CMS was ±0.012. The average standard deviation across CMS1 was ±0.004, CMS 2 was ±0.022, CMS 3 was ±0.001 and CMS 4 was ±0.023. Seven replicates from 2 unique samples varied from expected results. The reruns of 2 misclassified samples demonstrated nearly equally higher probability of CMS2 and CMS4, indicating more of mixed subtype (FIG. 8). Inter run reproducibility was assessed from 3 separate extractions from 4 unique patient samples for a total of 12 cases. These 12 cases were run across 3 separate NanoString Runs and by 2 technologists. There was 100% concordance for the CMS classification among all 3 runs with an average standard deviation of ±0.002 for the gene expression score. The inter tech reproducibility was 100% for the CMS classification between both technicians with an average standard deviation of ±0.002 for the CMS subtype probability. Intra run reproducibility was assessed among 4 samples run in triplicate on a single Nanostring run. There was 100% concordance for the CMS classification among all 3 runs with an average standard deviation of ±0.012 for the CMS subtype probability (FIG. 9).

Prognostic Relevance of CMS by the Nanostring CMS Classifier: Table 16 shows clinicopathologic features and frequency of CMSs in patient enrolled in the phase II clinical trial or the ATTACC protocol. Using the Nanostring gene classifier, significant differences in overall survival were able to be identified by CMS. Specifically, patients with a CMS2 tumor had the best survival with a median of 40.2 months from stage IV diagnosis (95% CI: 30.8, 49.6), patients with a CMS1 or CMS3 tumor had the poorest survival after a stage IV diagnosis with median survival times of 22.5 (95% CI: 18.1, 26.9) or 22.3 (95% CI: 17.8, 26.8) months, respectively. Patients with a CMS4 tumor had a survival pattern that was in between that of CMS2 and CMS1 or CMS3 with a median survival time of 33.3 months (95% CI: 26.6, 40.0) (FIG. 10).

TABLE 16 Patient Characteristics of the cohorts utilized to correlate CMS subtype and overall survival (N = 247) Mean Age at Initial Diagnosis (SD) 51.0 (11.2) Mean Age at Stage IV Diagnosis (SD) 51.5 (11.3) Sex Male 131 (53.0) Female 116 (47.0) Race/Ethnicity NH White 182 (73.7) NH African American 20 (8.1) Hispanic 19 (7.7) NH Asian 18 (7.3) Other/Unknown 8 (3.2) Stage at Initial Diagnosis I 3 (1.2) II 17 (6.9) III 68 (27.5) IV 155 (62.8) Missing 4 (1.6) Consensus Molecular Subtype 1, Immune 10 (4.1) 2, Canonical 82 (33.2) 3, Metabolic 18 (7.3) 4, Mesenchymal 98 (39.7) Mixed 39 (15.8)

All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

REFERENCES

The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.

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What is claimed is:
 1. A method of classifying a cancer status of a subject having colorectal cancer, comprising: (a) obtaining a tumor sample from the subject; (b) measuring an expression level of a plurality of genes in the tumor sample, wherein each gene in the plurality of genes is selected from Table 1; (c) generating an expression profile based on a comparison between the expression level of the plurality of genes in the sample from the subject and a corresponding expression level obtained from a reference sample derived from a different subject having a known cancer status; and (d) categorizing the cancer status of the subject based on the expression profile.
 2. The method of claim 1, wherein step (c) comprises applying a weighted support vector machine to the expression level of the plurality of genes.
 3. The method of claim 1, wherein the plurality of genes comprises at least 75 genes selected from Table
 1. 4. The method of claim 1, wherein the plurality of genes comprises at least 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, or 195 genes selected from Table
 1. 5. The method of claim 1, wherein the plurality of genes comprises all 200 genes selected from Table
 1. 6. The method of claim 1, wherein the plurality of genes comprises the 75-gene classifier from Table
 1. 7. The method of claim 1, wherein the plurality of genes comprises the 100-gene classifier from Table
 1. 8. The method of claim 1, wherein the cancer status is categorized as CMS1, CMS2, CMS3, or CMS4.
 9. The method of claim 1, wherein expression level of the plurality of genes is measured by detecting a level of mRNA transcribed from the plurality of genes.
 10. The method of claim 1, wherein the expression level is measured using nanostring probes.
 11. The method of claim 10, wherein the nanostring probes hybridize to the target sequence listed in Table 1 for each of the plurality of genes.
 12. The method of claim 1, wherein the expression level of the plurality of genes is measured by detecting a level of cDNA produced from reverse transcription of mRNA transcribed from the plurality of genes.
 13. The method of claim 1, wherein the expression level of the plurality of genes is measured by detecting a level of polypeptide encoded by the plurality of genes.
 14. The method of claim 1, wherein the sample is a formalin-fixed, paraffin-embedded sample.
 15. The method of claim 1, wherein the sample is a fresh frozen sample.
 16. The method of claim 1, further comprising reporting the cancer status of the subject.
 17. The method of claim 16, wherein the reporting comprises preparing a written or electronic report.
 18. The method of claim 17, further comprising providing the report to the subject, a doctor, a hospital, or an insurance company.
 19. A method of assessing a likelihood of a subject having colorectal cancer exhibiting a clinically beneficial response to treatment, the method comprising: (a) obtaining a cancer status determined according to the method of any one of claims 1-18; and (b) assessing a likelihood of the cancer exhibiting a clinically beneficial response to treatment based on the cancer status.
 20. The method of claim 19, further comprising reporting whether the subject is likely to exhibit a clinically beneficial response to treatment.
 21. The method of claim 20, wherein reporting comprises preparing a written or electronic report.
 22. The method of claim 21, further comprising providing the report to the subject, a doctor, a hospital or an insurance company.
 23. The method of claim 19, wherein if the subject is determined to have a CMS1 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.
 24. The method of claim 19, wherein if the subject is determined to have a CMS2 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with cetuximab, EGFR inhibitors, or HER2 inhibitors.
 25. The method of claim 19, wherein if the subject is determined to have a CMS3 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with cetuximab, EGFR inhibitors, or HER2 inhibitors.
 26. The method of claim 19, wherein if the subject is determined to have a CMS4 cancer, then the subject is likely to exhibit a clinically beneficial response to treatment with HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.
 27. A method of treating a patient having colorectal cancer, the method comprising obtaining a cancer status determined according to the method of any one of claims 1-18 and administering an anti-cancer therapy to the subject.
 28. The method of claim 27, wherein the anti-cancer therapy is a chemotherapy, a radiation therapy, a hormonal therapy, a targeted therapy, an immunotherapy or a surgical therapy.
 29. The method of claim 27, wherein if the subject is determined to have a CMS1 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.
 30. The method of claim 27, wherein if the subject is determined to have a CMS2 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor.
 31. The method of claim 27, wherein if the subject is determined to have a CMS3 cancer, then administering cetuximab, an EGFR inhibitor, or a HER2 inhibitor.
 32. The method of claim 27, wherein if the subject is determined to have a CMS4 cancer, then administering HSP90 inhibitors, bevacizumab, atorvastatin, 2-methoxyestradiol, indibulin, tipifarnib, or disulfiram.
 33. A composition comprising a set of nanostring probes that hybridize to the target sequences for at least 75 of the genes listed in Table
 1. 34. The composition of claim 33, wherein the composition comprises nanostring probes that hybridize to the target sequences for at least 80, 85, 90, or 95 of the genes listed in Table
 1. 35. The composition of claim 33, wherein the composition comprises nanostring probes that hybridize to the target sequences for all 100 genes in the 100 gene set listed in Table
 1. 36. The composition of claim 33, wherein the composition comprises nanostring probes that hybridize to the target sequences for at least 100, 110, 120, 130, 140, 150, 160, 170, 180, 185, 190, or 195 of the genes in the 200 gene set listed in Table
 1. 37. The composition of claim 33, wherein the composition comprises nanostring probes that hybridize to the target sequences for all 200 genes in the 200 gene set listed in Table
 1. 